A Comprehensive Exploration of L-UNet Approach: Revolutionizing Medical Image Segmentation

Abstract

In recent years, deep learning (DL) has become indispensable in medical image segmentation (MIS), proven by numerous studies showcasing its effectiveness. This paper presents two significant original contributions and conducts a comprehensive thematic evaluation of DL approaches in MIS. Diverging from traditional surveys that categorize DL literature into multiple groups and analyze individual works within each category, we adopt a multi-level classification approach. This method organizes existing literature from a broad overview to finer details. Furthermore, to address the inherent challenges of this field, we introduce an innovative method called L-UNet. The L-UNet method strategically utilizes filters with reduced parameters to construct a U-shaped convolutional neural network. This not only demonstrates impressive performance but also alleviates complexities associated with architectural parameters. Extensive evaluation across multiple datasets, including lung image segmentation, PH2 skin cancer segmentation, liver image segmentation, Chest X-ray segmentation, and COVID-19 chest X-ray segmentation, highlights the exceptional performance of L-UNet. Notably, experimental results showcase competitive accuracies, achieving figures such as 99.15% for lung image segmentation, 99.53% for liver image segmentation, 95.45% for PH2 skin cancer segmentation, and 98.99% for Chest X-ray segmentation. Moreover, for COVID-19 chest X-ray segmentation, a commendable accuracy of 93.45% is observed. The simplification of L-UNet renders it a highly practical choice for deployment in resource-limited environments or real-world scenarios necessitating faster performance and enhanced efficiency.

Keywords

• CNN
• biomedical image segmentation
• U-Net
• Link-Net
• CLAHE