Authors : K. Vinay; D. Lohith; S. Suresh Babu; Sunil Babu Melingi

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/yr5drktx

DOI : https://doi.org/10.38124/ijisrt/26may403

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Breast cancer continues to be the main cancer type which threatens women’s lives across all countries while creating a significant number of cancer deaths. The medical field requires immediate and accurate assessment of biopsy histopathological images which must be divided into benign and malignant categories to improve patient care through early medical intervention. The research introduces a novel system which uses deep learning techniques together with ensemble learning techniques to conduct breast cancer histopathological image classification for its binary classification task using BreakHis dataset. The proposed method establishes ResNet50 as an everlasting feature extractor which produces 2048dimensional deep feature vectors from microscopic tissue images through its Global Average Pooling (GAP) function after being pretrained on ImageNet.

Keywords : Breast Cancer, Histopathology, Resnet50, XG-Boost, Transfer Learning, Deep Feature Extraction, Breakhis, Binary Classification, Computer-Aided Diagnosis, Convolutional Neural Network.

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