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Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 12 Documents
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Search results for , issue "Vol. 8 No. 2 (2022): October" : 12 Documents clear
Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images Jessica Millenia; Mohammad Farid Naufal; Joko Siswantoro
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.149-161

Abstract

Background: Melanoma is a skin cancer that starts when the melanocytes that produce the skin color pigment start to grow out of control and form a cancer. Detecting melanoma early before it spreads to the lymph nodes and other parts of the body is very important because it makes a big difference to the patient's 5-year life expectancy. Screening is the process of conducting a skin examination to suspect a mole is melanoma using dermoscopic or macroscopic images. However, manual screening takes a long time. Therefore, automatic melanoma detection is needed to speed up the melanoma detection process. The previous studies still have weakness because it has low precision or recall, which means the model cannot predict melanoma accurately. The distribution of melanoma and moles datasets is imbalanced where the number of melanomas is less than moles. In addition, in previous study, comparisons of several CNN transfer learning architectures have not been carried out on dermoscopic and macroscopic images. Objective: This study aims to detect melanoma using the Convolutional Neural Network (CNN) with transfer learning on dermoscopic and macroscopic melanoma images. CNN with Transfer learning is a popular method for classifying digital images with high accuracy. Methods: This study compares four CNN with transfer learning architectures, namely MobileNet, Xception, VGG16, and ResNet50 on dermoscopic and macroscopic image. This research also uses black-hat filtering and inpainting at the preprocessing stage to remove hair from the skin image. Results: MobileNet is the best model for classifying melanomas or moles in this experiment which has 83.86% of F1 score and 11 second of training time per epoch. Conclusion: MobileNet and Xception have high average F1 scores of 84.42% and 80.00%, so they can detect melanoma accurately even though the number of melanoma datasets is less than moles. Therefore, it can be concluded that MobileNet and Xception are suitable models for classifying melanomas and moles. However, MobileNet has the fastest training time per epoch which is 11 seconds. In the future, oversampling method can be implemented to balance the number of datasets to improve the performance of the classification model.
Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning Subroto Singha; Priyangka Roy
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.218-225

Abstract

Background: Skin cancer can quickly become fatal. An examination and biopsy of dermoscopic pictures are required to determine if skin cancer is malignant or benign. However, these examinations can be costly. Objective: In this research, we proposed deep learning (DL)-based approach to identify a melanoma, the most dangerous kind of skin cancer. DL is particularly excellent in learning traits and predicting cancer. However, DL requires a vast number of images. Method: We used image augmentation and transferring learning to categorise images into benign and malignant. We used the public ISIC 2020 database to train and test our models. The ISIC 2020 dataset classifies melanoma as malignant. Along with the categorization, the dataset was examined for variation. The training and validation accuracy of three of the best pre-trained models were compared. To minimise the loss, three optimizers were used: RMSProp, SGD, and ADAM. Results: We attained training accuracy of 98.73%, 99.12%, and 99.76% using ResNet, VGG16, and MobileNetV2, respectively. We achieved a validation accuracy of 98.39% using these three pre-trained models. Conclusion: The validation accuracy of 98.39% outperforms the prior pre-trained model. The findings of this study can be applied in medical science to help physicians diagnose skin cancer early and save lives.   Keywords: Deep Learning, ISIC 2020, Pre-trained Model, Skin Cancer, Transfer Learning

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