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All Journal Teknika Jurnal CoreIT JIEET (Journal of Information Engineering and Educational Technology) INOVTEK Polbeng - Seri Informatika Sebatik Krea-TIF: Jurnal Teknik Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Ilmu Komputer Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Febi Yanto, Febi
UIN Suska Riau
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : INOVTEK Polbeng - Seri Informatika

 1 
Pengaruh Image Enhancement Contrast Stretching dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Hatta, M Ilham; Afrianty, Iis; Afriyanti, Liza
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4233

Abstract

Kidney tumors are the third most common after prostate and bladder tumors, accounting for around 208,500 cases (2%) of all cancer cases globally. Renal Cell Carcinoma constitutes 85% of these cases, transitional cell cancer 12%, and other types 2%. In Indonesia, the incidence is 3 per 100,000 people, with a male-to-female ratio of 3.2:1. Ultrasound, CT scans, and MRI are used to detect, diagnose, and assess kidney tumors, with CT scans being crucial for evaluating complex lesions, both cystic and solid. This study uses the Image Enhancement Contrast Stretching technique to improve CT-Scan image quality for deep learning classification using the EfficientNet-B0 architecture. The dataset is split into training, validation, and testing sets in an 80:20 ratio. Hyperparameters include Adamax and RAdam optimizers with learning rates of 0.01, 0.001, and 0.0001. The highest performance was achieved using the Image Enhancement Contrast Stretching technique with the RAdam optimizer and a learning rate of 0.01, resulting in 100% accuracy, precision, recall, and F1-score. For the original dataset using the Adamax optimizer with a 0.01 learning rate, the highest performance was 99.12% accuracy, 98.28% precision, 100% recall, and 99.13% F1-score. This technique significantly enhances the performance of kidney tumor classification models.
Pengaruh Contrast Limited Adaptive Histogram Equlization dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Jannata, Nanda; Handayani, Lestari; Cynthia, Eka Pandu
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4235

Abstract

The human excretory system, comprising the kidneys, ureters, and bladder, plays a crucial role in maintaining overall body health by filtering blood and eliminating waste products, including water and toxins. However, kidneys are susceptible to various diseases, such as kidney tumors, which present a significant global health challenge, with over 430,000 new cases reported in 2020. This research focuses on using CT-scan imaging techniques to analyze and assess kidney tumors. The study employs the Image Enhancement Contrast Limited Adaptive Histogram Equalization (CLAHE) method to enhance the quality of Kidney Tumor CT-Scan images for deep learning classification using the MobileNetV2 Architecture. The dataset, consisting of 4,560 images, is divided into training, validation, and testing sets in an 80:20 ratio. Applying CLAHE with a clip limit of 20 and an 8x8 tile grid significantly improves evaluation metrics compared to non-CLAHE datasets, achieving an impressive f1-score of 99.56% and accuracy of 99.56%. This improvement is achieved using the Adam optimizer with a learning rate of 0.01. These findings underscore the efficacy of CLAHE in enhancing the model's performance in kidney tumor classification. They are particularly valuable for radiologists as they enhance diagnostic accuracy and efficiency, potentially reducing diagnostic errors and improving patient outcomes.
Co-Authors Adha, Martin Afriyanti, Liza Afroni, Hallend Agustian, Surya Agustina, Auliyah Dewi, Nurika Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Elvia Budianita Fadhilah Syafria Fadhilah Syafria Fauziyyah, Laila Nurul Fitri Insani Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Harni, Yulia Hatta, M Ilham Herwanza, Niken Aisyah Maharani Idhafi, Zaky Iis Afrianty Irma Welly, Irma Irsyad , Muhammad Iwan Iskandar Jannata, Nanda Lestari Handayani Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Muhammad Fahri Muhammad Fikry Muhammad Irsyad Nabyl Alfahrez Ramadhan Amril Nazruddin Safaat H Negara, Benny Sukma Permata, Rizkiya Indah Pizaini Pizaini, Pizaini Prananda, Alga Putra, Wahyu Eka Rahmad Abdillah Rahman, Muhammad Taufikur Reski Mai Candra Shahira, Fayza Sofiyah, Wan Sugandi, Hatami Karsa Suwanto Sanjaya Suwanto Sanjaya Syafria, Fadhillah Wijaya, Andy Huang Wirdiani, Putri Syakira Yenggi Putra Dinata Yuli Novita Sari, Yuli Novita Yusra Yusra, Yusra