An Innovative Technique for Medical Image Segmentation Using Convolutional Neural Networks Optimized Through Stochastic Gradient Descent
DOI:
Abstract
Medical image segmentation is crucial due to its essential role in disease therapy. Various challenges such as hair artifacts, illumination variations, and different imaging acquisitions complicate this task. In this paper, we introduce a novel convolutional neural network (CNN) architecture designed to address these challenges. We also compare our method with two well-known architectures, Unet and FCN, to demonstrate the superiority of our approach. Our results, evaluated using four metrics, accuracy, dice coefficient, Jaccard index, and sensitivity show that our method outperforms the other two. We employed Jaccard distance and binary cross-entropy as the loss functions and used SGD+Nesterov as the optimization algorithm, which proved more effective than the Adam optimizer. In the preprocessing step, we included image resizing to speed up the process and image augmentation to enhance the evaluation metrics. As a postprocessing step, we applied a threshold technique to the algorithm's outputs to increase the contrast of the final images. This method was tested on two well-known and publicly available medical image datasets: PH2 for melanoma detection and Chest X-ray images for detecting chest lesions or COVID-19.
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