Training CNN-based Model on Low Resource Hardware and Small Dataset for Early Prediction of Melanoma from Skin Lesion Images

• Published in: Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5; no. 2; pp. 41 - 46
• Main Authors: Parmonangan, Ivan Halim, Marsella, Marsella, Pardede, Doharfen Frans Rino, Rijanto, Katarina Prisca, Stephanie, Stephanie, Kesuma, Kreshna Adhitya Chandra, Cahyaningtyas, Valentina Tiara, Anggreainy, Maria Susan
• Format: Journal Article
• Language: English
• Published: 31.05.2023
• ISSN: 2686-2573; 2686-2573
• DOI: 10.21512/emacsjournal.v5i2.9904

Melanoma is a kind of rare skin cancer that can spread quickly to the other skin layers and the organs beneath. Melanoma is known to be curable only if it is diagnosed at an early stage. This poses a challenge for accurate prediction to cut the number of deaths caused by melanoma. Deep learning methods have recently shown promising performance in classifying images accurately. However, it requires a lot of samples to generalize well, while the number of melanoma sample images is limited. To solve this issue, transfer learning has widely adapted to transfer the knowledge of the pretrained model to another domain or new dataset which has lesser samples or different tasks. This study is aimed to find which method is better to achieve this for early melanoma prediction from skin lesion images. We investigated three pretrained and one non-pretrained image classification models. Specifically, we choose the pretrained models which are efficient to train on small training sample and low hardware resource. The result shows that using limited sample images and low hardware resource, pretrained image models yield better overall accuracy and recall compared to the non-pretrained model. This suggests that pretrained models are more suitable in this task with constrained data and hardware resource.