Kidney Disease Segmentation and Classification Using Firefly Sigma Seeker and MagWeight Rank Techniques

Deep learning models possess the ability to precisely analyze medical images such as MRI, CT scans, and ultrasound images. This automated diagnostic process facilitates the early detection of kidney disease by identifying any abnormalities or signs of disease. Consequently, it allows for timely intervention and treatment, while also reducing the need for manual interpretation by radiologists or clinicians. As a result, the diagnosis process is expedited, leading to improved efficiency in healthcare. The proposed technique focuses on enhancing parallel convolutional layer architectures in kidney disease segmentation through the utilization of advanced optimization techniques. This approach integrates Firefly Sigma Seeker and MagWeight Rank methodologies into the design of these architectures. The Firefly Sigma Seeker methodology dynamically adjusts key parameters related to standard deviation during training to enable early stopping in the initial phase. Subsequently, MagWeight Rank optimizes parameter weighting and ranking within the architecture to prune less important weights, thereby reducing computational time and overfitting. By leveraging these techniques, the parallel convolutional layers are specifically tailored for kidney disease segmentation tasks. Finally, the Multi-Stream Neural Network (MSNN) efficiently classifies kidney disease. Through extensive experimentation and evaluation on kidney disease segmentation datasets, a comparative analysis of different architectures was conducted in terms of segmentation accuracy, computational efficiency, and scalability. The proposed framework achieves optimal segmentation performance, with an accuracy of 98.2%, a minimized loss of 0.1, a reduced computational time of 15 min and 4 s, and successfully avoids overfitting.