A Novel Algorithm for Breast Cancer Detection: An Overview

Abstract

Breast cancer stands as a significant global health issue impacting millions of women. Detecting it early is pivotal for enhancing the prognosis and survival rates of affected individuals. Recent years have witnessed a surge in research dedicated to crafting innovative algorithms and employing machine learning techniques to facilitate early breast cancer diagnosis. These cutting-edge approaches harness diverse imaging modalities and computational methods to elevate accuracy and efficiency. This study introduces a distinctive algorithm designed to forecast the percentage of breast cancer through the analysis of mammogram images. The algorithm incorporates a variety of techniques to enhance its accuracy and overall performance. These methodologies include data augmentation, dropout layers, the RMSprop optimizer with learning rate decay, sparse categorical crossen tropy loss, and an increased number of training epochs. Data augmentation is employed to generate a diverse set of training examples by applying random transformations to the images. This process enriches the model's ability to generalize to unseen data. Dropout layers, strategically placed after Conv2D and Dense layers, serve as a preventive measure against overfitting, thereby improving the model's generalization capabilities. The use of the RMSprop optimizer with learning rate decay offers precise control over the learning rate during training, enabling faster convergence and potentially reaching a more optimal solution. A thorough analysis of these features allows the algorithm to predict the probability and percentage of breast cancer in a given patient. The results demonstrate a strong correlation between the algorithm's predictions and the actual percentage of breast cancer, highlighting its accuracy and reliability. In an extensive cohort study, the algorithm exhibited exceptional accuracy in predicting the percentage of breast cancer, surpassing traditional methods in both sensitivity and specificity. The introduction of this algorithm holds great promise in supporting healthcare professionals with the early detection and diagnosis of breast cancer. It anticipates advancements in patient outcomes and the formulation of personalized treatment strategies.Its multifaceted approach, integrating various techniques, positions it as a robust solution in the ongoing efforts to enhance breast cancer diagnosis and patient care.