A Comparative Study To Classify Malignant Skin Lesion Using Mask-RCNN

Vaidya, Nikhil (2022) A Comparative Study To Classify Malignant Skin Lesion Using Mask-RCNN. Masters thesis, Dublin, National College of Ireland.

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Abstract

Rapidly progressing melanomas of the skin can be lethal if they go undetected for an extended period. Recent studies have found that 1/3 of all cancers are skin cancers. If it remains undiagnosed, the disease can cause severe harm or even be fatal for the patient. Thus, it is essential to detect melanoma early and begin treatment immediately. Conventional medical diagnosis typically requires painful and expensive skin samples taken from the patient. Melanoma may be detected quickly and effectively using contemporary AI (Artificial Intelligence) and deep learning techniques. This study employs a novel method for melanoma identification by applying automated custom annotation on Mask R-CNN with ResNet101/50 backbones to classify lesions. The publically accessible ISIC Dataset (International Skin Imaging Collaboration) is used to perform this task. Mean accuracy and recall (mAP and mAR) ratings are used to grade the model. Using our model, we got an mAP score of 78.6 and an mAR of 97.6. The effectiveness of the models is evaluated via a thorough comparison.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Skin Lesion; Melanoma; Data Augmentation; Custom Annotation; Mask R-CNN; Deep Neural Networks; ResNet101/50 backbone
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	14 Mar 2023 12:25
Last Modified:	14 Mar 2023 12:25
URI:	https://norma.ncirl.ie/id/eprint/6331

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