International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
End to End Breast Cancer Detection System Dr. Gulab Singh Chauhan1, Debopam Chowdhury2, Chaitra Adiga3, Nithin HN4, Anoj Kumar Gupta5 1 Professor, Department of Information Science and Engineering,
Acharya Institute of Technology, Bangalore, Karnataka, India 2,3,4,5 Students, Department of Information Science and Engineering, Acharya Institute of Technology, Bangalore, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Breast cancer remains a leading global health
scalable automated diagnostic tools underscores the urgent need for scalable diagnostic tools.
challenge with more than 2.3 million new cases and 670,000 deaths reported in 2022. Early detection is critical as untreated tumors become metastatic and drastically reduce survival rates. Traditional diagnostic methods face limitations including reliance on specialized radiologists high falsepositive rates (20%) and delayed detection in underserved areas. This paper presents an end-to-end breast cancer detection system with deep learning as its main goal and using the information from a cluster to optimize diagnostic accuracy. The system integrates mammogram imaging with patient metadata (age, breast laterality and implant status) using a fine-tuned EfficientNetV2-B3 model to achieve 84% accuracy in classifying abnormalities as benign or mal Key outputs include malignancy probability, invasiveness grading and prediction confidence metrics deployed via a Djangobased web platform for real-time analysis. By reducing diagnostic time from days to minutes and prioritizing highrisk cases this solution minimizes unnecessary biopsies, democratize access to precision diagnostic. Key Words: Breast cancer, deep learning, malignancy probability, convolutional neural network (CNN), healthcare inequity, mammogram analysis, falsepositive rate, Django platform.
1.INTRODUCTION Breast cancer is among the deadliest cancers in the world with survival rates dropping sharply if diagnosis is delayed [1]. While early detection improves survival rates by up to 95%, traditional methods are resource-intensive errorprone and inaccessible in underserved regions.
Table -1: Breast cancer Cases Globally (2022) The above table and below table shows the number of cases reported and the number of female died due to breast cancer globally in 2022, this data shows that how underdeveloped or developing countries are in top position in table of people died and how developed nations are way lower in numbers because of their advancement and ability of detect and diagnose it early. Thereby it’s critical that we diagnose and treat the cancer in earlier stage there is less chance of death , hence we can see from data how essential its to diagnose at first hand if some female has a mass or tumor in her breast to treat.
1.1 Global Burden and Diagnostic Challenges In 2022 the disease’s disproportionate impact across economic strata was estimated at 2.9 million new cases and 670,000 deaths. High-HDI regions like Asia benefit from efficient screening, reducing mortality to 1 in 71 while lowHDI regions like Africa face a mortality rate of 1 in 48 due to delay detection and limited healthcare access. The lack of
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