This document discusses using machine learning for breast cancer detection. It begins with an introduction on breast cancer prevalence and existing machine learning methods. It then discusses breast cancer conditions in India, noting rising case numbers. Key factors in late diagnosis are identified as lack of awareness programs and low participation. The proposed methodology uses CNN for automated feature extraction to distinguish malignant from benign tumors faster. It describes preprocessing, augmentation, model testing and accuracy evaluation. Risk factors, signs, and prevention strategies are outlined. A schematic diagram and steps of the detection process are provided. The conclusion notes high accuracy achieved and potential for early detection to improve outcomes.

1. BREAST CANCER DETECTION
SCHOOL OF COMPUTING SCIENCE AND ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
GALGOTIAS UNIVERSITY, GREATER NOIDA,INDIA
2021-2022
Submitted By :- Under The Supervision of :-
Chandrajeet Jha (19SCSE1010397) Ms. SONIA KUKREJA
Bipul Raj (19SCSE1010599)
Project ID: BT3148

2. INTRODUCTION
 Breast cancer signifies unique of the diseases that as more losses each year. Breast
cancer is the utmost collective cancer amongst women universal secretarial for
25% of all cancer cases and pretentious 2.1 million persons in 2015 primary
diagnosis suggestively rises the likelihoods of persistence.
 The existing methods are Machine learning, method of training machines with
data to make the decision for same conditions and its application can be observed
in various domains such as medical, network, object identification and security
etc. There are 2 machine learning types that is single and hybrid approaches as for
instance Support vector machine.

3. CONDITION IN INDIA
 Breast cancer has been ranked as the most common cancer among Indian women
and accounts for 27% of all cancers in women. About 1 in 28 women are likely to
develop breast cancer during their lifetime. In the urban areas, the incidence is 1 in
22 as compared to the rural areas where 1 in 60 women develop breast cancer.
 In India, the number of breast cancer cases are rising. According to 2018 report of
breast cancer statistics, there are 1,62,468 new registered cases and 87,090
reported deaths. Cancer survival becomes more difficult in higher stages of its
growth and more than 50% of Indian women suffer from stage 3 and 4 of breast
cancer.

4. FACTORS
• Advanced stage of breast cancer diagnosis in India revolves around two factors:
1. Non-existent of breast cancer awareness programs as compared to developed
countries.
2. Non-participation of women on a mass scale if any such programs exist.

5. METHODOLOGY
• The proposed methodology will help us to distinguish between malignant and
benign tumor at a faster rate. CNN being a complex and complicated classifier can
extract vital features automatically without depending on preprocessing. It is more
proficient because it filters the important parameters and also is flexible being
capable to work exceptionally well on image data.
• The importance of feature selection in a machine learning model is inevitable. It
turns the data to be free from ambiguity and reduces the complexity of the data.
Also, it reduces the size of the data, so it is easy to train the model and reduces the
training time. It avoids over tting of data. Selecting the best feature subset from all
the features increases the accuracy. Some feature selection methods are wrapper
methods, lter methods, and embedded methods.

6. Signs of breast cancer may include:
 Lump in the breast or underarm (armpit)
 Swelling or thickening of all or part of the breast
 Dimpling or skin irritation of breast skin
 Localized, persistent breast pain
 Redness, scaliness or thickening of the nipple or breast skin
 Nipple discharge (other than breast milk)
 Any change in the size or shape of the breast

7. Risk factors for breast cancer
Age: The chance of getting breast cancer increases as women age. Nearly 80 percent of
breast cancers are found in women over the age of 50
History of breast cancer: A woman who had breast cancer in one breast is at an
increased risk of developing cancer in her other breast
Family history of breast cancer: A woman has a higher risk of breast cancer if her
mother, sister or daughter had breast cancer, especially at a young age (before 40).
Having other relatives with breast cancer may also raise the risk
Genetic factors: Women with certain genetic mutations, including changes to the
BRCA1 and BRCA2 genes, are at higher risk of developing breast cancer during their
lifetime. Other gene changes may raise breast cancer risk as well.
Childbearing and menstrual history: The older a woman is when she has her first
child, the greater her risk of breast cancer. Also at higher risk are:
 Women who menstruate for the first time at an early age (before 12)
 Women who go through menopause late (after age 55)
 Women who never had children (nulliparous)

8. SCHEMATIC DIAGRAM

9. STEPS
Select the input image
Pre-processing
Image Augmentation
Building Convolutional Architecture
Model Testing
Testing for output
Accuracy Testing
Result Comparison with actual dataset Select the input image

10. •AWARENESS: Get to and stay at a healthy weight. Balance your food
intake with physical activity to avoid excess weight gain.
•Be physically active. Every week, get at least 150 minutes of moderate
intensity or 75 minutes of vigorous intensity activity (or a combination of
these).
•Limit or avoid alcohol. The ACS recommends that women have no more

11. CONCLUSION
Breast cancer detection using machine learning has achieved successfully with
accuracy up to 97.4%. By using this machine learning the output is effective and faster
and reduces the complexity.
Here we have used combination of classifiers & algorithms such as decision tree
algorithm, random algorithm and logistic regression helped to achieve high accurate
and efficient model. The results shown in decision tree classifiers prediction and in the
actual classification of the patients which presenting ones as malignant (cancerous) and
zeros as benign (non- cancerous).
This model can predict a greater number of correct values than negatives. By detecting
the breast cancer at early stage, the cancer can be curable and the patients can avoid
painful surgeries. The overall computational time for the preprocessing would be 3.5sec
& the time for the processing stage would be around 5sec for the number of dataset
considered, this time could vary depending upon the number of dataset that has been
chosen.

12. RESULT
• The analysis of traditional ML reveals the limited usage of the methods, whereas
the DL methods have great potential for implementation in clinical analysis and
improve the diagnostic capability of existing CAD systems.
• Breast Cancer if detected at an early stage will help save the lives of thousands
of women or even men. These projects help real-world patients and doctors
gather as much information as possible. Research on nine papers has helped us
collect data for the project we propose. Using machine learning algorithms, we
will be able to classify and predict cancers into whether they are malignant or
malignant.
• Machine learning algorithms can be used for medical oriented research, it
advances systems, minimizes human errors and reduces manual mistakes

13. THANK YOU