Abstract
Class imbalance is a dominant challenge in medical image segmentation when dealing with MRI images from highly imbalanced datasets. This study introduces a comprehensive, multifaceted approach to enhance the accuracy and reliability of segmentation models under such conditions. Our model integrates advanced data augmentation, innovative algorithmic adjustments, and novel architectural features to address class label distribution effectively. To ensure the multiple aspects of training process, we have customized the data augmentation technique for medical imaging with multi-dimensional angles. The multi-dimensional augmentation technique helps to reduce the bias towards majority classes. We have implemented novel attention mechanisms, i.e., Enhanced Attention Module (EAM) and spatial attention. These attention mechanisms enhance the focus of the model on the most relevant features. Further, our architecture incorporates a dual decoder system and Pooling Integration Layer (PIL) to capture accurate foreground and background details. We also introduce a hybrid loss function, which is designed to handle the class imbalance by guiding the training process. For experimental purposes, we have used multiple datasets such as Digital Database Thyroid Image (DDTI), Breast Ultrasound Images Dataset (BUSI) and LiTS MICCAI 2017 to demonstrate the prowess of the proposed network using key evaluation metrics, i.e., IoU, Dice coefficient, precision, and recall.