Enhancing Medical Image Analysis through Deep Learning:

Abstract

Medical image analysis has become indispensable for accurate diagnosis and treatment planning. However, despite advances in deep learning, several critical challenges persist, ranging from more efficient models to the integration of multiple tasks within a unified

framework. This thesis addresses these challenges by proposing innovative deep learn- ing architectures that enhance medical image classification, segmentation, and multitask

learning. At the heart of this research is the goal of developing models that deliver

high performance and tackle the nuanced complexities of medical data. Existing clas- sification models often overlook valuable information hidden in the spectral domain of

images. I address this by integrating spatial and spectral features, demonstrating their complementary power to detect diseases such as COVID-19 from chest radiographs. This

approach facilitates a more holistic understanding of medical images, improving the ac- curacy and reliability of diagnostic systems. To further enhance image classification, I

explore hybrid architectures that combine convolutional and transformer-based models. These models leverage the strengths of both architectures, capturing fine-grained visual details and long-range dependencies. This significantly improves various medical imaging datasets, offering deeper interpretability and superior classification accuracy, particularly

in complex diagnostic scenarios. Moving beyond classification, I tackle the fundamen- tal challenge of segmenting complex and irregular regions within medical images, where

traditional deep learning models often struggle. To overcome this, I introduce a novel segmentation framework that combines the power of deep neural networks with trainable

morphological operations. This leads to a more precise delineation of regions of inter- est, even in challenging clinical scenarios, setting a new benchmark for medical image

segmentation. One of the most pressing issues in medical imaging is the inefficiency of current multitask learning models, which often require vast computational resources

and struggle to generalize across different tasks. I present a lightweight multitask learn- ing framework that excels at both segmentation and classification, particularly in breast

tumor analysis. Using novel morphological attention mechanisms and the sharing of task- specific knowledge, proposed model significantly reduces computational complexity while

improving performance. Importantly, this framework demonstrates versatility across various medical imaging domains, from gland segmentation and malignancy detection in histology images to skin lesion analysis, demonstrating its robustness and applicability in real-world settings. Altogether, this thesis offers solutions to some of the most pressing problems in medical image analysis, providing models that are not only more accurate but also computationally efficient, making them suitable for deployment in clinical practice.