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All Journal Indonesian Journal of Life Sciences
Sadrawi, Muammar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Medicine & Pharmacology

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Artificial Intelligence Based Brain Tumor Localization Using YOLOv5 Sadrawi, Muammar; Fugaha, Daniel Ryan; Heerlie, Devita Mayanda; Lorell, Juan; Gautama, Nicolaas Raditya Putra; Aminuddin, Mohamad Zafran
Indonesian Journal of Life Sciences 2023: IJLS Vol 05 No .01
Publisher : Indonesia International Institute for Life Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54250/ijls.v5i01.176

Abstract

Brain tumor is a mutation in the brain cells in which the cells keep dividing. The earlier the tumor detected, the higher survival rate for the patient. This study develops the brain tumor detection system by utilizing the you only look once (YOLO). The model is based on YOLOv5 architect. The open dataset of tumorous images is utilized. From this dataset, the corresponding masks are given alongside the images. Our study tries to compare several YOLOv5 models to localize the brain tumor. The results show YOLOv5m, YOLOv5l, and YOLOv5x models have higher precision and recall values. The inference time from those models is relatively small for recent computational resources. In conclusion, the YOLOv5 models have produced superior result in localizing the brain tumor
Development of Long QT Syndrome Detection Using SciPy Himawan, Moses; Sadrawi, Muammar
Indonesian Journal of Life Sciences 2023: IJLS Vol 05 No .02
Publisher : Indonesia International Institute for Life Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54250/ijls.v5i02.178

Abstract

Long QT syndrome (LQTS) is a type of arrhythmia that manifests itself as the elongation of the QT interval. LQTS is caused due to different disorders in the sodium and potassium channels which results in reduced activity of the cardiac muscle. To diagnose LQTS, an algorithm is used to detect the elongated QT interval through detection of the peaks using Python. The current build of the algorithm is able to detect different ECG graphs for their QT interval with relative accuracy however is not capable of detecting the different components if the graph has too much noise or if they have irregular wavelengths due to other cardiovascular disease (CVD).
Co-Authors Aminuddin, Mohamad Zafran Fugaha, Daniel Ryan Gautama, Nicolaas Raditya Putra Heerlie, Devita Mayanda Himawan, Moses Lorell, Juan