Lung condition classifications

1. Lung Conditions Prognosis Using CNN Model
BY HARSHIT JAIN

2. INDEX

3. INTRODUCTION
• Different sorts of disorders that impair the lungs' normal function are referred to
as Lung Diseases. They have an impact on pulmonary and respiratory processes,
including breathing and lung health.
• There are several lung conditions which are brought on by bacteria, viruses, or
other fungal infections, environmental changes. Other variables such as asthma,
carcinoma, and mesothelioma, also contribute to lung disorders .
• The subsequent disorders for which our study, Lung Condition Prognosis have
provided are Infiltration, Pneumonia, Hernia, Atelectasis, Cardiomegaly, Effusion,
Mass, Nodule, Consolidation and Pneumothorax.

4. PROBLEM AND THEORIES
PROBLEMS
• We are using CNN and not Artificial Intelligence because one problem with using
artificial intelligence in medicine is that there isn't enough data.
• The medical industry makes substantial use of machine learning techniques.
Finding hidden patterns in enormous amounts of data that is utilized for clinical
diagnostics has a lot of potential with data mining.
• Health businesses may use data mining to analyze data systematically, find
inefficiencies, and pinpoint best practices that enhance patient care while
reducing costs.
• Identification of lung disorders is one of the largest challenges, and several
researchers are working to assist physicians by creating sophisticated algorithms
for making medical judgments.

5. PROBLEM AND THEORIES
THEORY
• Deep learning strategies categories were learned gradually owing to their hidden layer
design by initially producing low-level categories like letters, then high-level categories
like words, and finally high-level categories like sentences. The network's neurons and
nodes generated a complete representation of the picture, with each representing a
different aspect of the whole.
• The main benefit of deep learning algorithms was that they tried to gradually learn high-
level qualities from data. As a result, hard-core feature extraction and domain expertise
were no longer necessary
• We have used CNN for the tissue pattern classification using mammographic images and
it shows the outstanding performance.
• The main objective was to create a prediction engine that will enable consumers to
determine if they have lung illness while sitting at home.

6. METHODS
• The first step was to create a custom X-Ray dataset for the 10 lung diseases which we collected
them from different labs and hospitals.
• After that, we used different object extraction models to extract the lungs from the X-Ray images
i.e., the required region and remove the unwanted regions in order to decrease the expense, and
time, and to improve the model's ability to forecast lung ailments.
• The task was to detect the diseases from abstracted lung images for which some deep learning
algorithms were used. We used CNN in which the feature extraction was done before flattening the
image. In this, the features were extracted, and the dimensions were reduced.
• Template matching was done which is a technique used in CNN to find a small part of the image. A
filter was generated here which was moving and generally of a 3x3 matrix, and that filter did match
with every pixel.

7. METHEDOLOGY FLOW CHART
• Firstly, the input data is taken and after that
preprocessing is performed on the data
inputted after preprocessing, which is
removal of missing values, noises in the data
etc.
• After that, the data bifurcation is performed
that is dividing the dataset into test and
train data and this has been done as we
cannot train the model on single dataset and
if we did so then it will not be able to assess
the performance of the model. Therefore,
there is a need to separate the data into
train test and validation datasets.
• After that the model is trained on the train
dataset and the resulting model M1 will be
used to validate the test dataset and the
final decision will be made i.e., finding or no
finding.

8. RESULT
DATASET
• The dataset used here for the implementation of this research work consists of data images of
lungs collected from different hospitals.
• In this research work, 10 different diseases have been tested. The data images have been divided
into two parts as one part is used for training and another part is used for testing process.
• The training data set consists of 90 images of every disease we are testing, and the testing dataset
consists of 10 images of each disease which in total makes 100 images of each disease utilized in
carrying out the research work.

9. RESULT
EXPERIMENTAL ANALYSIS
• The experiment carried out for this work was based on the self-developed CNN model.
• The developed CNN model consists of an input layer of size 600 × 600 × 3, dropout layer, average
pooling layer and dense layer.
• For every layer ‘relu’ activation function is used.
• After passing all images to the model, a total of 64,952,958 parameters are extracted.
• Among these parameters, 64,801,534 are used for training purposes and the remaining 151,424
are treated as non-trainable features.

10. RESULT
TRAINING AND VALIDATION LOSS CURVE OF USED MODEL.

11. RESULT
TRAINING AND VALIDATION ACCURACY CURVE

12. RESULT
• After the execution, the model is evaluated for 35 epochs with early-stopping
features. Therefore, the experiment stops at epoch number 12.
• The obtained results are measured in terms of Training loss , training accuracy,
validation loss and Validation accuracy.
• The obtained accuracy of the model after 13th epoch is like 90.6 % of training
accuracy, 0.1838 is training loss, 82.6 % of validation accuracy and 0.2396 is
validation error.
• The error and validation accuracy can be further enhanced by using various
parameters tunning. This can be performed in the next work.

13. CONCLUSION
• This work presents the working of different CNN for the automated detection of eleven different
lung diseases using chest X-Ray images.
• The self-designed CNN model has been used for the study and performance of the model is
computed in terms of the training accuracy, testing accuracy, training loss and validation loss.
• This study aimed to achieve accurate and error-free prediction of diseases while using minimal
manpower and small model architectures.
• To increase the accuracy of the work, the segmentation of the lung’s X-Ray images was carried
out which was a crucial step in order to reach precision using radiographs. It eliminated the noisy
data which was not required for the prediction of diseases.
• The study done has proved to be helpful in the fast and accurate detection of lung diseases and
has the potential to save many lives that are lost due to incorrect and delayed diagnoses.

14. REFERENCES
1. Frade, J., Pereira, T., Morgado, J. et al. Multiple instances learning for lung patho-physiological findings
detection using CT scans. Med Biol Eng Comput 60, 1569–1584 (2022). https://doi.org/10.1007/s11517-
022-02526-y
2. AliboniAlibi L, Pennati F, Gelmini A, Colombo A, Cioni A, Milanese G, Sverzellati N, Magnani S, Vespro V,
Blasi F, Aliverti A. Detection and classification of bronchiectasis through convolutional neural networks.
Journal of Thoracic Imaging. 2022 Mar 13; 37(2):100-8.
3. Chauhan, R., Ghanshala, K. K., & Joshi, R. C. (2018, December). Convolutional neural network (CNN) for
image detection and recognition. In 2018 First International Conference on Secure Cyber Computing and
Communication (ICSCCC) (pp. 278-282). IEEE.
4. Tiwari, P., Pant, B., Elarabawy, M. M., Abd-Elnaby, M., Mohd, N., Dhiman, G., & Shar-ma, S. (2022). Cnn
based multiclass brain tumor detection using medical imaging. Computa-tional Intelligence and
Neuroscience, 2022.
5. Bian Y, Zheng Z, Fang X, Jiang H, Zhu M, Yu J, Zhao H, Zhang L, Yao J, Lu L, Lu J. Ar-tificial Intelligence to
Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocar-cinoma. Radiology. 2022 Sep 6:220329.

15. THANK YOU