Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements

Abstract

Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer

“…F1 Score: The F1 Score serves as the weighted average of Precision and Recall. Consequently, this score considers both false positives and false negatives, offering a balanced assessment of a classifier’s performance ( 22 ). It is a valuable metric to demonstrate that a classifier attains high values for both recall and precision.…”