Aditya Vaibhav Chiduruppa* and Praveen Kumar Pandian Shanmuganathan

Florida Institute of Technology, Florida, USA

*Corresponding Author: Aditya Vaibhav Chiduruppa, Florida Institute of Technology, Florida, USA.

Received: September 02, 2025; Published: October 14, 2025

Abstract

Breast cancer is a disease that has existed for thousands of years [1], and affects millions of people yearly [2]. There were 8.8 million cancer deaths and 684,996 breast cancer deaths in 2020 alone [3]. Any amount of work that we do to reduce the number of deaths by early diagnosis of breast cancer is going to be crucial in saving several lives in a year. We set out to be able to design an easier way to diagnose breast cancer, using machine learning to help. A patient who is at risk of having breast cancer is suggested to have a biopsy. The AI tool we have created aids in the process of the biopsy and helps with the diagnosis. This would increase the efficiency of the patient workflow by adding the ML models in the loop before it gets to the physician, lowering cost and increasing the number of people they would be able to look at. The data we initially tried to use was from mammograms, but due to the large number of existing research using mammography images, we switched to using histopathology images.

Histopathology images are images of small portions of tissue under a microscope, sampled from a biopsy. Histopathology images are also complex in terms of disease classification for an untrained eye which could add a lot of false classifications. Hence the tool could aid in assisting technicians to physicians in their diagnosis. After testing with several different machine learning models, we landed on MobileNet V2 which gave an accuracy of 91.2% across the dataset. This tool could add a lot of value in clinical diagnosis during the patient pathway.

Keywords: Breast Cancer; Histopathology; Neural Networks

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Citation

Citation: Aditya Vaibhav Chiduruppa* and Praveen Kumar Pandian Shanmuganathan. “Detecting Breast Cancer in Histopathology with Deep Neural Networks".Acta Scientific Cancer Biology 9.4 (2025): 34-37.

Copyright

Copyright: © 2025 Adhikari Ganga D., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.