A Novel Deep Learning-Based Framework for Efficient Content-Based Medical Image Retrieval
Keywords:
Medical image retrieval, deep learning, content-based retrieval, GLCM, ANN, CLSTM, breast cancer, tuberculosis, Alzheimer's disease, brain tumor, COVID-19.Abstract
In clinical research and decision-making, medical image retrieval is essential. In this article, we provide a brand-new framework for retrieving content-based medical images that is based on deep learning. The framework is designed to retrieve relevant medical images from five distinct categories: 'Breast Cancer', 'Tuberculosis', 'Alzheimer's Disease', 'Brain Tumor', and 'COVID-19'. We employ the Gray Levels Co-occurrence Matrix (GLCM) & concentrate on six distinct aspects: "dissimilarity," "correlation," "homogeneity," "contrast," "ASM" (Angular Second Moment), & "energy" in order to extract significant information from the medical images. These features provide important insights into the texture and structure of the images, enabling effective discrimination between different medical conditions. For training the retrieval framework, we employ two models: Artificial Neural Network (ANN) and the Convolutional Long Short-Term Memory (CLSTM) hybrid deep learning model. The ANN model achieves an accuracy of 91% in classifying the medical images, while the CLSTM model outperforms it with an accuracy of 99.01%. Our test results show how well the suggested framework works at quickly retrieving pertinent medical photos. The integration of deep learning techniques enhances the accuracy of image classification and improves the retrieval performance. The framework has potential applications in medical research, diagnosis, and treatment planning by enabling quick and accurate access to relevant medical images for specific conditions.
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