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All Journal Statistika Jurnal Peluang Proceedings of Annual International Conference Syiah Kuala University - Social Sciences Chapter Jurnal Gaussian Jurnal Pengajaran MIPA Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam Jurnal Riset Pendidikan Matematika Jurnal Elemen Eduma : Mathematics Education Learning and Teaching Jurnal Informatika Upgris Jurnal Penelitian Pendidikan IPA (JPPIPA) Numeracy : Jurnal Ilmiah Pendidikan Matematika International Journal for Educational and Vocational Studies JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) ESTIMASI: Journal of Statistics and Its Application Journal of Applied Data Sciences Narra J International Journal of Education and Digital Learning (IJEDL) Jurnal Natural Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) Heca Journal of Applied Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science PESARE: Science and Engineering Service Journal Jurnal Didaktik Matematika
Muhammad Subianto
Department Of Statistics, Faculty Of Mathematics And Natural Science, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Journal : Narra J

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Evaluation of atopic dermatitis severity using artificial intelligence Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Bulqiah, Mikyal; Idroes, Ghazi M.; Niode, Nurdjannah J.; Sofyan, Hizir; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.511

Abstract

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
Psoriasis severity assessment: Optimizing diagnostic models with deep learning Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Prakoeswa, Cita RS.; Kairupan, Tara S.; Idroes, Ghifari M.; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 4 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i3.1512

Abstract

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen’s Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2–93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.
Co-Authors . Zulfan Afidh, Razief Perucha Fauzie Ahmad, Noor Atinah Ainal Mardhiah Akhyar, Fikrul ALFIAN FUTUHUL HADI Almunir Sihotang Asep Rusyana Azzahra, Syarifah Fathimah Baehaqi Bagus Sartono Cut Morina Zubainur Cut Mulyawati Cut Rina Rossalina Dwi Fadhiliani Earlia, Nanda Essy Harnelly EVI RAMADHANI Farsiah, Laina Fitriana AR Furqany, Nuwairy El Ghazi Mauer Idroes Hijriyana P., Meildha Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. INA YATUL ULYA Indah Manfaati Nur Irnanda , Irnanda, Irnanda Irvanizam, Irvanizam Jamil, Muhammad Salsabila Kairupan, Tara S. Kurniadinur, Kurniadinur M. Ikhsan M. Ikhsan M. Ikhsan Maulana, Aga Miftahuddin Miftahuddin Miftahuddin Miftahuddin Miftahuddin Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Al Agani Muhammad Iqbal Muhammad Irfan Mukhamad Najib Mursyida, Waliam Nazaruddin Niode, Nurdjannah Jane Nisya Fajri Noviandy, Teuku R. Nurbaiti Nurbaiti Nurdjannah J. Niode Nurjani Nurjani Nurjannah Nurjannah Nurleila, Nurleila Prakoeswa, Cita RS. Purnama Mulia Farib Rahmah Johar Razief Perucha Fauzie Afidh Reza Wafdan Rika Fitriani Rika Siviani Rinaldi Idroes Rizal Munadi RR. Ella Evrita Hestiandari S.Pd. M Kes I Ketut Sudiana . Salmawaty Salmawati Salmawaty Salmawaty Sasmita, Novi Reandy sufriani, sufriani Sugara, Dimas Rendy Suhartono Suhendra, Rivansyah Suryadi Suryadi Teuku Rizky Noviandy Tuti Asmiati Vivi Dina Melani Vivi Dina Melani Vivi Dina Melani Widya Sari Wira Dharma Wisnu Ananta Kusuma Yusrizal Yusrizal Zahriah, Zahriah Zainal Abidin Zainal Abidin Zhilalmuhana, Teuku Zulfan