Affiliations: [a] Department of Information Technology, St. Joseph’s College of Engineering, Chennai, India
| [b] Data Engineer, Sirius – A CDW Company, Ramanujam IT City, Tharamani, Chennai, India
Correspondence:
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Correspondence to: Dr. V. Muthulakshmi, Professor, Department of Information Technology, St. Joseph’s College of Engineering, Old Mahabalipuram Road, Chennai, India. E-mail: [email protected].
Abstract: The advent of deep learning techniques has ignited interest in medical image processing. The proposed work in this paper suggests one of the edge technologies in deep learning, which is recommended, based on a Radiomics feature extraction model for the effective detection of Kaposi sarcoma, a vascular skin lesion expression that indicates the most prevalent cancer in AIDS patients. This work investigates the role and impact of medical image fusion on deep feature learning based on ensemble learning in the medical domain. The model is crafted wherein the pre-built ResNet50 (Residual network) and Visual Geometry Group (VGG16) are fine-tuned and an ensemble learning approach is applied. The pre-defined CNN was incrementally regulated to determine the appropriate standards for classification efficiency improvements. Our findings show that layer-by-layer fine-tuning can improve the performance of middle and deep layers. This work would serve the purpose of masking and classification of skin lesion images, primarily sarcoma using an ensemble approach. Our proposed assisted framework could be deployed in assisting radiologists by classifying Kaposi sarcoma as well as other related skin lesion diseases, based on the positive classification findings.
