Optimized Hybrid Model Framework for Breast Cancer Classification

Breast diagnosis from pathology reports is a widely employed clinical method for diagnosing breast tumors. But it's challenging due to low contrast, high noise, and diverse appearances. Experienced professionals rely on factors like global context, local geometry, and intensity changes, acquired through years of clinical experience. To ease the burden on doctors, we propose integrating machine learning with diagnosis practice. However, effective and efficient breast tumor detection is crucial for automated disease diagnosis. In the last decade, various deep learning models have emerged for breast tumor detection. Traditional methods lack real-time responsiveness, accuracy, and scalability. To address these issues, we introduce an enhanced framework with hybrid model based on a multi-task cascaded convolutional network. This framework leverages a substantial dataset of clinically confirmed images with precise labels, employing a multi-task cascaded architecture. It involves two stages of deep convolutional networks designed to detect and recognize tumor affected area progressively, ensuring real-time operation and high accuracy. Additionally, we introduce an improved feature fusion-based augmentation method, enhancing diagnostic model performance. Our experiments demonstrate exceptional accuracy and time efficiency. Furthermore, the model's versatility is highlighted in the context of related diseases. Our proposed model showed a high accuracy of 97.69 for multi classification and 99.39 for binary classification. Also, our proposed framework when compared with many other hybrid model utilized less time (3.19 seconds per image) and resources making it efficient.