Breast Cancer Detection Using Hybrid Ri-vit In Histopathalogical Images

Abstract

Breast Cancer Remains A Significant Global Health Concern, Impacting Millions Of Women Each Year. Timely Detection And Precise Diagnosis Are Essential To Enhancing Treatment Success And Lowering Death Rates. Histopathological Imaging Is Widely Utilized For Diagnosing Breast Cancer, But Interpreting These Images Accurately Often Requires Specialized Medical Expertise, Which May Not Be Readily Available In All Clinical Environments. The Dataset Used In This Study Comprises Breast Tissue Images Labeled To Reflect The Presence Or Absence Of Cancer. A Convolutional Neural Network (CNN) Was Employed To Automatically Extract Meaningful Features From The Images, Followed By A Fully Connected Layer To Perform Classification. The Model Was Optimized By Minimizing Prediction Error Using A Suitable Loss Function And Optimization Technique. To Assess Its Effectiveness, The Model's Performance Was Measured Using Metrics Such As Accuracy.

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