A study of the Intelligent Hybrid Image Processing Model Development for Breast Cancer Detection using AI-ML

Abstract

The article gives a brief study of the breast cancer related issues. Breast cancer continues to be one of the most prevalent and life-threatening diseases among women worldwide, making early detection and accurate diagnosis a global healthcare priority. This M.Tech. project, titled “Modelling, Analysis, Design & Development of Novel Hybridized Bio-medical Image Processing Algorithms for Detection/Prediction, Classification of Breast Cancer Disease using Federated Explainable AI based ML Models,” aims to develop an intelligent and privacy-preserving diagnostic framework that enhances the reliability and interpretability of automated breast cancer detection. The work focuses on bridging the gap between medical imaging, artificial intelligence, and ethical data utilization by designing a comprehensive end-to-end system capable of assisting clinicians in early diagnosis and treatment planning. The work targets the development of an advanced hybrid pre-processing model that improves image quality by minimizing noise and enhancing tissue visibility in mammogram and histopathological images. By integrating wavelet-based denoising and adaptive contrast enhancement techniques, the model ensures that subtle structural variations within breast tissues are preserved, forming a strong foundation for precise analysis. The objective addresses the segmentation challenge by introducing a hybrid deep learning-based approach, combining convolutional neural networks and attention-driven U-Net architectures to isolate tumors or regions of interest (ROIs) with high precision. This stage enhances the reliability of feature extraction and forms the core of the image-based diagnostic workflow.