Enhancing cancer diagnostics through a novel deep learning-based semantic segmentation algorithm: A low-cost, high-speed, and accurate approach.

Deep learning-based semantic segmentation approaches provide an efficient and automated means for cancer diagnosis and monitoring, which is important in clinical applications. However, implementing these approaches outside the experimental environment and using them in real-world applications requires powerful and adequate hardware resources, which are not available in most hospitals, especially in low- and middle-income countries. Consequently, clinical settings will never use most of these algorithms, or at best, their adoption will be relatively limited. To address these issues, some approaches that reduce computational costs were proposed, but they performed poorly and failed to produce satisfactory results. Therefore, finding a method that overcomes these limitations without losing performance is highly challenging. To face this challenge, our study proposes a novel, optimal convolutional neural network-based approach for medical image segmentation that consists of multiple synthesis and analysis paths connected through a series of long skip connections. The design leverages multi-scale convolution, multi-scale feature extraction, downsampling strategies, and feature map fusion methods, all of which have proven effective in enhancing performance. This framework was extensively evaluated against current state-of-the-art architectures on various medical image segmentation tasks, including lung tumors, spleen, and pancreatic tumors. The results of these experiments conclusively demonstrate the efficacy of the proposed approach in outperforming existing state-of-the-art methods across multiple evaluation metrics. This superiority is further enhanced by the framework's ability to minimize the computational complexity and decrease the number of parameters required, resulting in greater segmentation accuracy, faster processing, and better implementation efficiency.