Cancer is malignant growth or tumour which forms due to an uncontrolled division of cells in a part of
body which may even lead to death. These are of different types depending upon the part of body affected.
If it is Pancreas then the disease is termed as Pancreatic Cancer. This paper presents an Artificial Neural
Network model to diagnose pancreatic cancer based on a set of symptoms. An ANN model is created after
analysing the actual procedure of disease diagnosis by the doctor. An approach to detect various stages of
cancer affected in pancreas is presented in the paper. Results of the study suggest the advantage of using
ANN model instead of manual disease diagnosis.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Design of an Intelligent System for Improving Classification of Cancer DiseasesMohamed Loey
The methodologies that depend on gene expression profile have been able to detect cancer since its inception. The previous works have spent great efforts to reach the best results. Some researchers have achieved excellent results in the classification process of cancer based on the gene expression profile using different gene selection approaches and different classifiers
Early detection of cancer increases the probability of recovery. This thesis presents an intelligent decision support system (IDSS) for early diagnosis of cancer-based on the microarray of gene expression profiles. The problem of this dataset is the little number of examples (not exceed hundreds) comparing to a large number of genes (in thousands). So, it became necessary to find out a method for reducing the features (genes) that are not relevant to the investigated disease to avoid overfitting. The proposed methodology used information gain (IG) for selecting the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the Gray Wolf Optimization algorithm (GWO). Finally, the methodology exercises support vector machine (SVM) for cancer type classification. The proposed methodology was applied to three data sets (breast, colon, and CNS) and was evaluated by the classification accuracy performance measurement, which is most important in the diagnosis of diseases. The best results were gotten when integrating IG with GWO and SVM rating accuracy improved to 96.67% and the number of features was reduced to 32 feature of the CNS dataset.
This thesis investigates several classification algorithms and their suitability to the biological domain. For applications that suffer from high dimensionality, different feature selection methods are considered for illustration and analysis. Moreover, an effective system is proposed. In addition, Experiments were conducted on three benchmark gene expression datasets. The proposed system is assessed and compared with related work performance.
Breast cancer diagnosis machine learning pptAnkitGupta1476
This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
PET scan plays an important role in gynecological malignancies by identifying malignant cells with high sensitivity. It has applications in initial staging of cancers of the cervix, ovary, and endometrium by detecting lymph node and distant metastases. PET scan is also useful for detecting recurrence through increased glucose uptake in cancer cells and can change treatment plans in up to 30% of cases. Overall, PET-CT provides benefits as a single imaging test for accurate staging and response assessment in gynecological cancers.
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
Design of an Intelligent System for Improving Classification of Cancer DiseasesMohamed Loey
The methodologies that depend on gene expression profile have been able to detect cancer since its inception. The previous works have spent great efforts to reach the best results. Some researchers have achieved excellent results in the classification process of cancer based on the gene expression profile using different gene selection approaches and different classifiers
Early detection of cancer increases the probability of recovery. This thesis presents an intelligent decision support system (IDSS) for early diagnosis of cancer-based on the microarray of gene expression profiles. The problem of this dataset is the little number of examples (not exceed hundreds) comparing to a large number of genes (in thousands). So, it became necessary to find out a method for reducing the features (genes) that are not relevant to the investigated disease to avoid overfitting. The proposed methodology used information gain (IG) for selecting the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the Gray Wolf Optimization algorithm (GWO). Finally, the methodology exercises support vector machine (SVM) for cancer type classification. The proposed methodology was applied to three data sets (breast, colon, and CNS) and was evaluated by the classification accuracy performance measurement, which is most important in the diagnosis of diseases. The best results were gotten when integrating IG with GWO and SVM rating accuracy improved to 96.67% and the number of features was reduced to 32 feature of the CNS dataset.
This thesis investigates several classification algorithms and their suitability to the biological domain. For applications that suffer from high dimensionality, different feature selection methods are considered for illustration and analysis. Moreover, an effective system is proposed. In addition, Experiments were conducted on three benchmark gene expression datasets. The proposed system is assessed and compared with related work performance.
Breast cancer diagnosis machine learning pptAnkitGupta1476
This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
PET scan plays an important role in gynecological malignancies by identifying malignant cells with high sensitivity. It has applications in initial staging of cancers of the cervix, ovary, and endometrium by detecting lymph node and distant metastases. PET scan is also useful for detecting recurrence through increased glucose uptake in cancer cells and can change treatment plans in up to 30% of cases. Overall, PET-CT provides benefits as a single imaging test for accurate staging and response assessment in gynecological cancers.
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
This document summarizes applications of artificial intelligence in pathology. It discusses machine learning and deep learning techniques used for tasks like cancer detection and classification from histopathology images. Examples are given of using AI for breast, lung, prostate, brain, ovarian and cervical cancer analysis from whole slide images and digital pathology. Applications in immunohistochemistry, genetic mutation prediction and tumor detection for molecular analysis are also summarized.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
Review on automated follicle identification for polycystic ovarian syndromejournalBEEI
This document reviews different methods that have been proposed for automated follicle identification in ultrasound images to diagnose polycystic ovarian syndrome (PCOS). Various image segmentation techniques are discussed, including watershed transform, region growing, edge-based, thresholding, clustering, and active contour methods. For each technique, example studies applying the method are described along with their performance evaluations in terms of metrics like recognition rate, misidentification rate, and computational time. The highest recognition rate of 87.5% was achieved using morphological operations with edge-based segmentation, but that study did not consider follicle size which is important for PCOS diagnosis. Overall, the review finds that accurate identification of small follicles remains a challenge and suggests further research is needed
In vivo characterization of breast tissue by non-invasive bio-impedance measu...ijbesjournal
Biological tissues have complex electrical impedance related to the tissue dimension, the internal structure
and the arrangement of the constituent cells. Since different tissues have different conductivities and
permittivities, the electrical impedance can provide useful information based on heterogeneous tissue
structures, physiological states and functions. In vivo bio-impedance breast measurements proved to be a
dependable method where these measurements can be adopted to characterize breast tissue into normal
and abnormal by a developed normalized coefficient of variation (NCV) as a numerical criterion of the bioimpedance
measurements. In this study 26 breasts in 26 women have been scanned with a homemade
Electrical Bio-impedance System (EBS). Characteristic breast conductivity and permittivity measurements
emerged for Mammographically normal and abnormal cases. CV and NCV are calculated for each case,
and the value of NCVs greater than 1.00 corresponds to abnormalities, particularly tumours while NCVs
less than 1.00 correspond to normal cases. The most promising results of (NCV) for permittivity at 1 MHz,
it detects 73% of abnormal cases including 100% tumor cases while it detects 82% of normal cases. The
numerical criterion NCV of in-vivo bio-impedance measurements of the breast appears to be promising in
breast cancer screening.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Digital Pathology, FDA Approval and Precision MedicineJoel Saltz
Digital pathology platforms combined with machine learning can improve the consistency and quality of clinical decision making by precisely scoring known criteria from pathology images and predicting treatment outcomes and cancer types. Researchers are developing tools to extract features from pathology images, link these features to molecular data and clinical outcomes, and use these integrated datasets to gain new insights into cancer and select the best interventions. The SEER Virtual Tissue Repository aims to enable population-level cancer research by creating a linked collection of de-identified clinical data and whole slide images from pathology samples that can be analyzed using computational methods.
Comparative Study between Rapid Diagnostic Tests and Microscopy for Diagnosis...Premier Publishers
This study compared the performance of rapid diagnostic tests (RDTs) to microscopy for diagnosing malaria in Seme Sub County, Kenya. Blood samples from 230 patients were tested using microscopy as the gold standard, as well as two RDTs - one detecting Histidine Rich Protein (HRP2) and another detecting both HRP2 and parasite lactate dehydrogenase (pLDH). The sensitivity of the HRP2 RDT was 94.44% compared to microscopy, and the sensitivity of the pLDH RDT was also 94.44%. Both RDTs had specificities above 85% compared to microscopy. The study found agreement between 89.13-88.70% for the RDTs and microscopy
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
IRJET- Breast Cancer Prediction using Supervised Machine Learning AlgorithmsIRJET Journal
This document describes a study that used machine learning algorithms to predict breast cancer. The researchers trained decision tree, logistic regression, and random forest models on a breast cancer dataset. They preprocessed the data, which included converting categorical variables to numeric. The random forest model achieved the best prediction accuracy of 98.6%. The study aims to help detect breast cancer at earlier stages to improve treatment outcomes.
IRJET- Breast Cancer Prediction using Support Vector MachineIRJET Journal
This document discusses using a support vector machine to predict whether breast cancer is benign or malignant. The SVM model is trained on the Wisconsin Diagnostic Breast Cancer dataset containing 569 samples with 32 features each. The SVM achieves 96.09% accuracy at classifying samples into benign versus malignant categories on the test data, demonstrating its effectiveness at predicting breast cancer diagnosis.
Mechanical signals inhibit growth of a grafted tumor in vivo proof of conceptRemy BROSSEL
We apply the principles of physical oncology (or mechanobiology) in vivo to show the effect of a “constraint field” on tumor growth.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152885
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...Cirdan
Automated image analysis has had a long history but continues to grow with massive improvements in algorithms, speed, performance, and with emerging opportunities for high throughput tissue biomarker analysis and automated decision support for primary diagnostics. Of particular importance is the development of computer vision and image analysis for H&E stained samples. This talk will outline recent advances in automated tissue analysis for biomarker discovery and diagnostics and how adoption of digital pathology will drive the demand for quantitative imaging and decision support.
As an example, PathXL have developed TissueMark for the automated identification and analysis of tumour in lung, colon, breast and prostate cancer digital H&E slides. The conventional pathological estimation of % tumour nuclei in H&E samples shows gross variation between pathologists, undermining the quality of next generation sequencing, molecular testing and patient therapy and potential of false negative diagnoses. TissueMark uses a combination of pattern recognition, glandular analysis and nuclear segmentation to identify premaligant and invasive cancer patterns in H&E stained tissues and use this to assess tumour cell numbers and annotate samples for nucleic acid extraction and molecular profiling. Benchmark data was generated to validate TissueMark technology and showed concordance of automated data with manual counts, accelerating tumour markup and improving sample quality assessment. This represents an example of how automated imaging of tissue samples can be of immense value in quantitative tumour analysis for molecular diagnostics, thereby improving reliability in discovery and diagnostics.
This together with other examples in pathology research and practice will demonstrate that next generation tissue imaging technology in digital pathology could radically change how pathology is practiced.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
PET scans use radioactive tracers to detect metabolic activity in cells that can indicate cancer. The document discusses PET scans and PET-CT scans, how they work, and their main uses in pulmonology such as characterizing lung nodules, staging lung cancer, detecting metastases, and monitoring treatment response. PET scans have high sensitivity and specificity for detecting malignant lung lesions and lymph node involvement compared to CT alone.
Breast Cancer Diagnostics with Bayesian NetworksBayesia USA
The Wisconsin Breast Cancer Database (WBCD) is a widely studied (and publicly available) data set from the field of breast cancer diagnostics. The creators of this database, Wolberg, Street, Heisey and Managasarian, made an important contribution with their research towards automating diagnostics with image processing and machine learning.
Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
This document summarizes a research paper that proposed a hybrid genetic algorithm and support vector machine (SVM) approach for breast cancer detection and feature selection. The proposed approach uses genetic algorithms to select the optimal features for an SVM classifier. It evaluated this approach on a breast cancer dataset and found that the sequential minimal optimization (SMO) SVM algorithm with genetic feature selection achieved very high accuracy, recall, and F-measure for classifying breast cancer compared to other classification algorithms. The genetic clustering algorithm was also able to accurately cluster the benign and malignant cases in the dataset within 38 seconds. In conclusion, the hybrid genetic algorithm and SVM approach provided an effective and accurate model for breast cancer detection and diagnosis.
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
This document describes a study that developed a neuro-fuzzy model to predict ovarian cancer malignancy preoperatively. The model uses demographic, serum tumor marker, and ultrasound criteria. It was trained using a database of 525 patients and evaluated for classification performance. The neuro-fuzzy model achieved an accuracy of 84% and an area under the ROC curve of 0.85 for discriminating between benign and malignant ovarian tumors, outperforming existing methods. The neuro-fuzzy approach combines the benefits of neural networks and fuzzy logic for modeling imprecise medical data while maintaining interpretability.
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
An enhanced liver stages classification in 3 d ct and 3d-us images using glrl...Bathshebaparimala
This document proposes a method to classify liver stages in 3D CT and 3D US images using texture feature extraction and 3D convolutional neural networks (CNNs). Gray level run length matrix (GLRLM) is used to extract texture features from segmented liver regions. A 3D CNN is then used for two stages of classification: first to classify images as normal or abnormal, and second to classify abnormal images into stages of liver disease (fatty liver, compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma). The method is implemented using TensorFlow and Keras in Python. Results show that 3D CT provides higher classification accuracy than 3D US.
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
This document summarizes applications of artificial intelligence in pathology. It discusses machine learning and deep learning techniques used for tasks like cancer detection and classification from histopathology images. Examples are given of using AI for breast, lung, prostate, brain, ovarian and cervical cancer analysis from whole slide images and digital pathology. Applications in immunohistochemistry, genetic mutation prediction and tumor detection for molecular analysis are also summarized.
IRJET- Breast Cancer Detection from Histopathology Images: A ReviewIRJET Journal
This document provides a review of techniques for detecting breast cancer from histopathology images. It discusses how histopathology examines tissue samples under a microscope to study diseases at a microscopic level. Detecting cell nuclei is an important first step, as is identifying mitosis (cell division) and metastasis (cancer spreading). The document reviews several techniques that use convolutional neural networks to automatically analyze histopathology images and detect breast cancer, including techniques for nuclei detection and segmentation. These automatic methods aim to assist pathologists by improving efficiency and reducing human error compared to manual analysis.
Review on automated follicle identification for polycystic ovarian syndromejournalBEEI
This document reviews different methods that have been proposed for automated follicle identification in ultrasound images to diagnose polycystic ovarian syndrome (PCOS). Various image segmentation techniques are discussed, including watershed transform, region growing, edge-based, thresholding, clustering, and active contour methods. For each technique, example studies applying the method are described along with their performance evaluations in terms of metrics like recognition rate, misidentification rate, and computational time. The highest recognition rate of 87.5% was achieved using morphological operations with edge-based segmentation, but that study did not consider follicle size which is important for PCOS diagnosis. Overall, the review finds that accurate identification of small follicles remains a challenge and suggests further research is needed
In vivo characterization of breast tissue by non-invasive bio-impedance measu...ijbesjournal
Biological tissues have complex electrical impedance related to the tissue dimension, the internal structure
and the arrangement of the constituent cells. Since different tissues have different conductivities and
permittivities, the electrical impedance can provide useful information based on heterogeneous tissue
structures, physiological states and functions. In vivo bio-impedance breast measurements proved to be a
dependable method where these measurements can be adopted to characterize breast tissue into normal
and abnormal by a developed normalized coefficient of variation (NCV) as a numerical criterion of the bioimpedance
measurements. In this study 26 breasts in 26 women have been scanned with a homemade
Electrical Bio-impedance System (EBS). Characteristic breast conductivity and permittivity measurements
emerged for Mammographically normal and abnormal cases. CV and NCV are calculated for each case,
and the value of NCVs greater than 1.00 corresponds to abnormalities, particularly tumours while NCVs
less than 1.00 correspond to normal cases. The most promising results of (NCV) for permittivity at 1 MHz,
it detects 73% of abnormal cases including 100% tumor cases while it detects 82% of normal cases. The
numerical criterion NCV of in-vivo bio-impedance measurements of the breast appears to be promising in
breast cancer screening.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Digital Pathology, FDA Approval and Precision MedicineJoel Saltz
Digital pathology platforms combined with machine learning can improve the consistency and quality of clinical decision making by precisely scoring known criteria from pathology images and predicting treatment outcomes and cancer types. Researchers are developing tools to extract features from pathology images, link these features to molecular data and clinical outcomes, and use these integrated datasets to gain new insights into cancer and select the best interventions. The SEER Virtual Tissue Repository aims to enable population-level cancer research by creating a linked collection of de-identified clinical data and whole slide images from pathology samples that can be analyzed using computational methods.
Comparative Study between Rapid Diagnostic Tests and Microscopy for Diagnosis...Premier Publishers
This study compared the performance of rapid diagnostic tests (RDTs) to microscopy for diagnosing malaria in Seme Sub County, Kenya. Blood samples from 230 patients were tested using microscopy as the gold standard, as well as two RDTs - one detecting Histidine Rich Protein (HRP2) and another detecting both HRP2 and parasite lactate dehydrogenase (pLDH). The sensitivity of the HRP2 RDT was 94.44% compared to microscopy, and the sensitivity of the pLDH RDT was also 94.44%. Both RDTs had specificities above 85% compared to microscopy. The study found agreement between 89.13-88.70% for the RDTs and microscopy
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
IRJET- Breast Cancer Prediction using Supervised Machine Learning AlgorithmsIRJET Journal
This document describes a study that used machine learning algorithms to predict breast cancer. The researchers trained decision tree, logistic regression, and random forest models on a breast cancer dataset. They preprocessed the data, which included converting categorical variables to numeric. The random forest model achieved the best prediction accuracy of 98.6%. The study aims to help detect breast cancer at earlier stages to improve treatment outcomes.
IRJET- Breast Cancer Prediction using Support Vector MachineIRJET Journal
This document discusses using a support vector machine to predict whether breast cancer is benign or malignant. The SVM model is trained on the Wisconsin Diagnostic Breast Cancer dataset containing 569 samples with 32 features each. The SVM achieves 96.09% accuracy at classifying samples into benign versus malignant categories on the test data, demonstrating its effectiveness at predicting breast cancer diagnosis.
Mechanical signals inhibit growth of a grafted tumor in vivo proof of conceptRemy BROSSEL
We apply the principles of physical oncology (or mechanobiology) in vivo to show the effect of a “constraint field” on tumor growth.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152885
Peter Hamilton on Next generation Imaging and Computer Vision in Pathology: p...Cirdan
Automated image analysis has had a long history but continues to grow with massive improvements in algorithms, speed, performance, and with emerging opportunities for high throughput tissue biomarker analysis and automated decision support for primary diagnostics. Of particular importance is the development of computer vision and image analysis for H&E stained samples. This talk will outline recent advances in automated tissue analysis for biomarker discovery and diagnostics and how adoption of digital pathology will drive the demand for quantitative imaging and decision support.
As an example, PathXL have developed TissueMark for the automated identification and analysis of tumour in lung, colon, breast and prostate cancer digital H&E slides. The conventional pathological estimation of % tumour nuclei in H&E samples shows gross variation between pathologists, undermining the quality of next generation sequencing, molecular testing and patient therapy and potential of false negative diagnoses. TissueMark uses a combination of pattern recognition, glandular analysis and nuclear segmentation to identify premaligant and invasive cancer patterns in H&E stained tissues and use this to assess tumour cell numbers and annotate samples for nucleic acid extraction and molecular profiling. Benchmark data was generated to validate TissueMark technology and showed concordance of automated data with manual counts, accelerating tumour markup and improving sample quality assessment. This represents an example of how automated imaging of tissue samples can be of immense value in quantitative tumour analysis for molecular diagnostics, thereby improving reliability in discovery and diagnostics.
This together with other examples in pathology research and practice will demonstrate that next generation tissue imaging technology in digital pathology could radically change how pathology is practiced.
IRJET- A Survey on Categorization of Breast Cancer in Histopathological ImagesIRJET Journal
This document summarizes various methods for categorizing breast cancer in histopathological images. It discusses machine learning and image processing techniques that have been used to build computer-aided diagnosis (CAD) systems to help pathologists diagnose breast cancer more objectively and consistently. The document reviews different classification methods that have been proposed, including those using fuzzy logic, level set methods, convolutional neural networks, texture features and ensemble methods. It concludes that accurately classifying histopathological images remains challenging due to limited publicly available datasets and variability in tissue appearance, but that machine learning and advanced image analysis offer promising approaches to improve cancer detection and diagnosis.
PET scans use radioactive tracers to detect metabolic activity in cells that can indicate cancer. The document discusses PET scans and PET-CT scans, how they work, and their main uses in pulmonology such as characterizing lung nodules, staging lung cancer, detecting metastases, and monitoring treatment response. PET scans have high sensitivity and specificity for detecting malignant lung lesions and lymph node involvement compared to CT alone.
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Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
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SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
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Cancer recognition from dna microarray gene expression data using averaged on...IJCI JOURNAL
Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer
incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and
curable in early stages, the vast majority of patients are diagnosed with cancer very late. Therefore, it is of
paramount importance to prevent and detect cancer early. Nonetheless, conventional methods of detecting
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promising technology that can detect cancerous cells in early stages of cancer by analyzing gene
expression of tissue samples. The microarray technology allows researchers to examine the expression of
thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based
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A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death
and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming
rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor
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magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors
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Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
This document reviews various machine learning algorithms that have been used to predict cervical cancer, including GLCM, SVM, k-NN, CNNs, MARS, PNNs, genetic algorithms, C5.0, RFT, and CART. It discusses past research on algorithms like K-means clustering, morphological operations, texture analysis, and thresholding that were applied to cervical cell images for segmentation and classification. The document also compares the merits and demerits of different algorithms in classifying cervical cancer cells, noting some achieved good accuracy while others had issues with over-segmentation, dependency on initial conditions, or not being suitable for overlapping cells.
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The document discusses using machine learning techniques to classify brain images as normal or abnormal. Over 700 unlabeled patient brain images would be labeled and preprocessed, then classified as "brain" or "not brain" using a neural network. Brain images would then be classified as normal or abnormal based on a convolutional neural network trained on labeled data. Related work applied similar techniques using autoencoders and GANs to detect abnormalities like tumors and lesions in MRI images, achieving classification accuracy from 68-62% compared to simpler thresholding methods. The unsupervised models generally outperformed supervised models at identifying anomalies.
Predicting Chronic Kidney Disease using Data Mining Techniquesijtsrd
Kidney is a significant aspect of a human body. Kidney infection or disappointments are expanded in every year. Presently a day’s chronic kidney infection is the most well known disease for the individuals. Today numerous individuals pass on due to chronic kidney disease. The principle issue of CKD is, it will influence the kidney gradually. A few people dont have side effects at all and are analysed by a lab test. It depicts the steady loss of kidney work. Early recognition and therapy are viewed as basic variables in the management and control of chronic kidney disease. Data mining techniques is utilized to extract data from clinical and laboratory, which can be useful to help doctors to recognize the seriousness stage of patients. Using Probabilistic Neural Networks PNN algorithm will get better prediction for determining the severity stage of chronic kidney disease. Seethal V | Kuldeep Baban Vayadande "Predicting Chronic Kidney Disease using Data Mining Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd37974.pdf Paper URL : https://www.ijtsrd.com/computer-science/data-miining/37974/predicting-chronic-kidney-disease-using-data-mining-techniques/seethal-v
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Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths worldwide.
Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is
preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late.
Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount
importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless,
conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not
very reliable. The microarray gene expression technology is a promising technology that couldpredict
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the-art machine learning based approach called averaged one-dependence estimators with subsumption
resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a
particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the
computational complexity, we employ an entropy-based geneselection approach to select relevant
prognosticgenes that are directly responsible for recurrence prediction. This proposed system has achieved
an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results
demonstrate the efficacy of our framework.
This chapter discusses rapid learning health care as an approach to enable customized radiotherapy. It describes a 4-phase methodology: 1) collecting diverse patient, treatment and outcome data, 2) developing prediction models using machine learning to analyze the data, 3) applying the models in clinical practice via decision support systems, and 4) evaluating predicted vs actual outcomes. The goal is to improve treatment predictability and ensure patients receive optimal therapy while efficiently using resources. Next steps involve including patient preferences in decision making for personalized cancer care.
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
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Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
DIFFERENT IMAGING MODALITIES USED FOR THE DETECTION OF PROSTATE CANCER – A RE...IRJET Journal
The document discusses various imaging modalities used to detect prostate cancer, including multiparametric ultrasound, multiparametric MRI, MRI-ultrasound fusion imaging, and positron emission tomography. It provides details on prostate anatomy, cancer grading, and treatment options to provide context. The modalities are compared in terms of their ability to detect characteristics like tissue alterations, angiogenesis, and metastatic spread. Limitations and potential improvements to the modalities are also reviewed.
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ARTIFICIAL NEURAL NETWORK FOR DIAGNOSIS OF PANCREATIC CANCER
1. International Journal on Cybernetics & Informatics (IJCI) Vol. 5, No. 2, April 2016
DOI: 10.5121/ijci.2016.5205 41
ARTIFICIAL NEURAL NETWORK FOR DIAGNOSIS OF
PANCREATIC CANCER
Sanoob M.U 1
, Anand Madhu2
, Ajesh K.R3
and Surekha Mariam Varghese4
Department of Computer Science and Engineering, Mar Athanasius College of
Engineering, Kothamangalam, Kerala
ABSTRACT
Cancer is malignant growth or tumour which forms due to an uncontrolled division of cells in a part of
body which may even lead to death. These are of different types depending upon the part of body affected.
If it is Pancreas then the disease is termed as Pancreatic Cancer. This paper presents an Artificial Neural
Network model to diagnose pancreatic cancer based on a set of symptoms. An ANN model is created after
analysing the actual procedure of disease diagnosis by the doctor. An approach to detect various stages of
cancer affected in pancreas is presented in the paper. Results of the study suggest the advantage of using
ANN model instead of manual disease diagnosis.
KEYWORDS
Neural Network, Diagnosis, Fuzzy Logic, Cancer, Pancreatic Cancer
1. INTRODUCTION
Artificial Neural Network (ANN) is a relatively raw model based on the brain’s neural structure.
In various clinical situations which are considered difficult, ANN has been used successfully as a
non-linear pattern recognition technique in making diagnostic and prognostic decisions [1]. Now
a days, many medical diagnosis problems are being solved using NN techniques. Artificial Neural
Networks are applied to medicine mainly for the task which is based on the measured features to
assign the patient to one of a small set of classes [2][3]. A number of researches are going on
worldwide on the applicability of neural networks in medical diagnosis [4][5]. Accuracy as well
as the objectivity of medical diagnosis has been increased using neural networks. In ANN, the
processing element is called as neurons. An artificial Neural Network is a network of such
interconnected neurons operating in parallel. Biological nervous systems are the main inspiration
behind the concept of artificial neurons. Functioning of a network greatly depends on the
connection between the elements in the network. These processing elements are subdivided into
several subgroups called layers in the network. The first and the last layers are called the input
and output layers respectively. There may be some additional layers, called hidden layers, in
between the input and output layers. A neural network can be trained to perform a particular
functionality. This is done by adjusting the values of the connections between the elements, called
weights. The number of cancer related deaths worldwide is increasing day by day and researches
shows that pancreatic cancer is the eighth most common cause of cancer-related deaths
worldwide and fourth worldwide.
Malignant type neoplasm pancreatic cancer is originated from transformed cells which arise in
tissues form the pancreas. Pancreas is a spongy organ that is around 6-inch long. This is located
2. International Journal on Cybernetics & Informatics (IJCI) Vol. 5, No. 2, April 2016
42
in the back of the abdomen behind the stomach. Pancreatic juices, insulin, and hormones are
created by exocrine and endocrine glands. These glands are contained within the pancreas. The
exocrine glands make the enzymes or pancreatic juices, which are then released to the intestines
through a series of ducts. This will help carbohydrates, proteins and fat to digest. Islets of
Langerhans are the small clusters of these endocrine cells. The glucagon and insulin are released
into the bloodstream by these islets of Langerhans. The levels of sugar in blood are managed by
two of these hormones. Improper working of these hormones will often result in diabetes.
Tumours are formed by the abnormal pancreas tissues continue splitting and create masses or
lumps of tissues. The major functions of pancreas are then interfered by these tumours. Benign is
the situation when a tumour stays at one location and shows limited growth.
This paper tried to show that if the particular condition of a patient is given, then the NN can be
used to make an accurate prognosis of each individual [6] [7]. How human intelligence can be
applied in health sector [8] [9] is the major concern behind this paper. A self-learning intelligent
system can be developed using NN which can overcome the uncertainties in the diagnosis of
pancreatic cancer. Some symptoms are taken from the patient’s previous medical records as well
as from the doctor, and by using these data the neural network model is trained to detect the
presence/absence of pancreatic cancer in that patient. To diagnose the pancreatic cancer properly
using this intelligent model fuzzified symptoms values are applied.
In prediction, NN models are widely being used, especially in medical diagnosis. Studies show
that for the diagnosis of different medical diseases, ANN have been used very successfully. In
2004, a NN based model is proposed by Kamruzzaman et al. for the diagnosis of heart diseases
[10]. In 2008, a Genetic Algorithm (GA) based technique for classifying tumour mass in breast
and to identify breast cancer has been introduced [11]. A new method for Predicting Blood
Cancer and Disorder is then developed by Payandeh [12] et al. later. One of the latest works in
this is Artificial Neural Network for predicting headache which is done by Bahar et al. In 2011
also, a lot of NN based disease diagnosis has been done. Artificial Neural Network to pre-
diagnosis of Hypertension [13], using Back-Propagation training algorithm, Artificial Neural
Network model to diagnose skin diseases by Backpo [14] et al. etc are some of them. Similarly
ANN models are also developed for breast cancer detection [15], Kidney stone diseases [16] etc.
In the following sections the paper will be dealing with the details of implementation of the ANN
model for detecting the pancreatic cancer. In section II, the Methodology used to in the paper is
discussed. The results of the experiment and Discussions are included in Section III. Then by
Section IV, the paper is concluded.
2. METHODOLOGY
The initial step towards performing the process of medical diagnosis was initiated by examining a
number of patients by a group of medical experts and identifying the symptoms. The next step
was to propose a neural network which could be used in diagnosing PC diseases. The proposed
model contains three layers namely input layer, hidden layer, and output layer. A single hidden
layer consisting of 20 hidden layer neurons was created and trained. The input samples and output
or target samples were divided for training, validation and test sets automatically.
For training the network, the training set was made use of. Training was continued until there was
network stops improving the validation set. The test set was completely independent of the
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measure of network accuracy. During the training phase, the patterns in data were learned by
hidden neurons and the relationship between input and output pairs are mapped. In the hidden
layer, a transfer function was used by each neuron for processing the data it receives from input
layer and the processed information to the output neurons was transferred, for further processing
using a transfer function in each neuron.
2.1. DATASET
Dataset used for the diagnosis of the pancreatic cancer is shown in Table 1. The data set consist
of 11 possible symptoms and 3 outcomes possible. The outcome is purely dependent on the
significance of symptoms for a particular patient. The entire dataset consists of measured features
of 120 patients in which 90 samples were used for training the network and the remaining for
testing purpose.
The set of symptoms represented as S = {Jaundice (J), Loss of Appetite (LA), Weight Loss (WL),
Pain in Upper Abdomen (PUA), Irritability (I), Gall Bladder Enlargement (GBE), Swelling
Lymph (SL), Diabetes Mellitus (DM), Deep Venous Thrombosis (DVT), Acholic Stool
&Steatorrhea (AS&S) and Fatty Tissue Abnormalities (FTA) }.
The set of possible outcomes of diagnosis represented as D = {Disease Detected, Disease might
be Detected and Disease not Detected}.
Disease outcomes and the corresponding label assigned for each of them is explained in Table 2.
Next, on the basis of fuzzy set, the paper describes each symptom by its membership value. The
basic block diagram that explains about the operational procedure is shown in Figure. 1.
Table 1. Symptoms Significance and Result
J LA WL PUA I GBE SL DM DVT AS&S FTA Remarks
0.45 0.26 0.60 0.80 0.30 0.10 0.35 0.15 0.55 0.72 0.18 1
0.18 0.00 0.62 0.59 0.17 0.78 0.82 0.50 0.14 0.36 0.47 2
0.73 0.69 0.32 0.33 0.25 0.55 0.13 0.20 0.61 0.49 0.86 2
0.24 0.63 0.28 0.08 0.39 0.36 0.70 0.55 0.23 0.17 0.63 3
0.59 0.68 0.43 0.75 0.73 0.29 0.37 0.13 0.70 0.55 0.43 1
0.44 0.53 0.56 0.69 0.57 0.63 0.19 0.41 0.34 0.72 0.38 1
0.52 0.63 0.72 0.30 0.60 0.19 0.40 0.21 0.42 0.39 0.52 3
0.13 0.12 0.63 0.24 0.11 0.47 0.23 0.51 0.69 0.10 0.63 2
0.63 0.38 0.33 0.21 0.49 0.72 0.62 0.24 0.77 0.43 0.40 1
0.24 0.55 0.78 0.30 0.54 0.41 0.78 0.43 0.64 0.18 0.23 1
Table 2. Assigned Labels and Outcome
Label Outcome
1 Detected
2 Might be Detected
3 Not Detected
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Figure 1. Operational Procedure
2.2. TRAINING OF PARAMETERS
The network will become ready to be trained, only once that network is organized and structured
for the targeted application. The initial weight has to be chosen at random for starting this
process. Training will be started after that. The network is trained by using existing set of data
which is directly obtained from various patients and whose output is well known. The neurons in
hidden layer will learn the data pattern while training and map relation between input pairs and
output pairs. Each hidden layer neuron made use of a transfer function for processing data that
accepts from input layer and transfers the information which is processed to the output neurons
for continuing the processing in each neuron using a transfer function.
3. RESULTS AND DISCUSSION
Matlab R2011a’s toolbox for Neural network is used for performance evaluation for the new
networks which consists of a 11 number three layer feed forward network of inputs and sigmoid
hidden neurons and linear output neurons is suggested. The new approach used Levenberg-
Marquardt algorithm for back propagation for training the network where training stops
automatically when generalization stops improving, as indicated by an increase in the MSE
(Mean Square Error) of the samples used for validation. The proposed neural network is shown in
Figure. 2.
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Figure 2. Proposed Neural Network
A membership based fuzzification scheme is adopted here for converting our dataset to to a
fuzzified set of symptoms. After an interview with physicians, a linear membership function was
again selected for each symptom. Three to five linguistic variables were assigned to each
symptom normally, and then repeated the classification tests.
The experimental results of implementing the new ANN methodology to distinguish between
Pancreatic Cancer affected and non-affected patients based upon specified symptoms represents
good capabilities of the network to learn the training patterns corresponding to symptoms of the
patients. The experimental setup is shown in Figure. 3
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Figure 3. FIS Editor
A triangular membership function is used for fuzzifying the inputs. Using Matlab, a membership
function editor is used. This Membership function editor is shown in the Figure. 4.
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Figure 4. Membership Function Editor
The performance graph plotted based on the results obtained is shown in Figure. 5.
Figure 5. Performance Plot
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4. CONCLUSION
An approach for the diagnosis of out-of-controlled cell growth in pancreas based on Artificial
Neural Network is explained in this paper. Detection of cancer in the pancreatic cells at its early
stage is necessary for its better treatment and cure. Hence it is important to detect pancreatic
cancer automatically to mitigate the real-world medical problems. Here we have presented how
effectively we can made use of neural networks in the detection and diagnosis of cancer in
pancreatic cells. The construction of a diagnostic system which is highly accurate based on neural
network model is done here. Also an investigation on the performance of neural network structure
is done in this paper. This model is designed in such a way that it made use of fuzzy values
instead of normal values for the incorporation of the neural network context. The model has
provision to accept symptoms in a patient and it will inform the present condition of that patient,
based on the evaluation made on the input symptoms. So this model is an interactive model. So
the early detection can be done and hence it will help the doctor to plan and do the better
medication for the patient. The symptoms are fuzzified and network is implemented based on
around 20 neurons, for getting the better performance and accurate diagnosis. Outcome of the
experiments indicates that the noval approach is able to valuate data in most efficient way
compared to other normal approaches. Future works can be extended for other similar disease
detection comprising complex and related datasets with similar or better accuracy..
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AUTHORS
Sanoob M.U. is currently pursuing M.Tech in Computer Science and Engineering in
Mar Athanasius College of Engineering. He completed his B.Tech from AdiShankara
Institute of Engineering and Technology, Kalady. His areas of research are Machine
Learning and Databases.
Ajesh K.R. is currently pursuing M.Tech in Computer Science and Engineering in
Mar Athanasius College of Engineering. He completed his B.Tech from AdiShankara
Institute of Engineering and Technology, Kalady. His areas of research are Machine
Learning and Image Processing.
AnandMadhu is currently pursuing M.Tech in Computer Science and Engineering in
Mar Athanasius College of Engineering. He completed his B.Tech from University
College of Engineering, Thodupuzha. His areas of research are Machine Learning and
Data Mining.
Surekha Mariam Varghese is currently heading the Department of Computer Science
and Engineering, M.A. College of Engineering, Kothamangalam, Kerala, India. She
received her B-Tech Degree in Computer Science and Engineering in 1990 from
College of Engineering, Trivandrum affiliated to Kerala University and M-Tech in
Computer and Information Sciences from Cochin University of Science and
Technology, Kochi in 1996. She obtained Ph.D in Computer Security from Cochin
University of Science and Technology, Kochi in 2009. She has around 25 years of
teaching and research experience in various institutions in India. Her research
interests include Network Security, Database Management, Data Structures and
Algorithms, Operating Systems, Machine Learning and Distributed Computing. She
has published 17 papers in international journals and international conference
proceedings. She has been in the chair and reviewer for many international
conferences and journals.