Citation

Ullah, Arif and Abdul Razak, Siti Fatimah and Hassnae, Remmach and Yogarayan, Sumendra and Rehman, Muhammad Zubair and Hameed, Abdul and Aznaoui, Hanane (2024) Analysis of Automated Melanoma Detection Utilizing Machine Learning and Deep Learning Techniques: A Review. International Journal of Image and Graphics. ISSN 0219-4678

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Abstract

Skin cancer is described as an abnormal, exponential growth of skin cells that originate from melanocytes due to DNA impair or damage. It is threatening because of its ability to metastasize to other body parts. Early diagnosis of melanoma can lessen the morbidity and mortality associated to skin cancer. Subjective visual inspection of melanoma may vary among investigators due to scarcity of medical tools and different level of experience. Therefore, accurate lesion detection becomes a tedious and time-consuming job. Several computer-aided diagnosis (CAD) systems based on machine and deep learning models have emerged in-order to assist clinicians in timely diagnosis of malignant (cancerous) melanoma. The conventional approaches derive low-level, handcrafted features from dermoscopy images of skin lesion. Novel deep learning-based neural networks are developed which aim to extract more generic and deep features for model training. Moreover, dermoscopy and clinical images play a central role in accurate detection of cancerous melanoma. This review paper is organized in five steps: First, we explain image analysis techniques of lesion images. Second, we highlight the challenges in identifying a lesion as melanoma or benign. Third, we provide an overview of publicly available skin lesion datasets. Fourth, we review the performance of various machine and deep learning-based melanoma diagnosis frameworks. This study exhibits that deep learning-based models and their ensemble outperform conventional machine learning approaches in respect of reliability, accuracy and sensitivity. The future work may be define bout the advisement of deep leaning algorithm.

Item Type:	Article
Uncontrolled Keywords:	Melanoma, skin cancer, dermoscopy, image processing, machine learning, deep learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jan 2025 03:13
Last Modified:	03 Jan 2025 03:13
URII:	http://shdl.mmu.edu.my/id/eprint/13273

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