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All Journal DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Ilmu Kesehatan
Casidi, Casidi
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Computer Science & IT Education Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Other

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Computer-Aided Diagnosis (CAD) of Stroke in The Brain CT-Scan Images Using Integration of Grey Level Co-Occurrence Matrix (GLCM) Texture Feature Extraction And K-Nearest-Neighbour (KNN) Classification Casidi, Casidi; Syukur, Abdul; Soeleman, M. Arief; Nurhindarto, Aris
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.646

Abstract

This study presents an advanced and efficient computer-aided diagnosis (CAD) system for stroke detection using brain CT images, integrating Grey Level Co-Occurrence Matrix (GLCM) feature extraction and K-Nearest Neighbour (KNN) classification. The objective is to enhance stroke detection accuracy and efficiency in clinical settings. A dataset of 400 brain CT images, divided into 300 for training and 100 for testing with equal normal and stroke classes, was used to evaluate performance. The GLCM texture features significantly differentiated between normal and stroke images. The optimized KNN model demonstrated high performance, achieving 99% classification accuracy, 100% sensitivity, 98% specificity, 97% precision, a 99% F1 score, 100% positive predictive value, and 98% negative predictive value. The average computation time per image was 3.2 seconds, indicating feasibility for real-time application. In conclusion, the GLCM-KNN integrated CAD system proves to be an accurate and efficient method for stroke diagnosis on brain CT scans, offering a potential solution for early stroke detection in resource-limited healthcare facilities.
Application of Stiching Method in Vertebrae Radiography ExaminationLong Spine for Scoliosis Cases: Case Study at Waled Regional Hospital Imamul Janna, Ahmad; Beni, Muhammad; Casidi, Casidi; Sinaga, Sunita
Jurnal Ilmu Kesehatan (JIKSAN) Vol. 1 No. 1 (2025): Jurnal Ilmu Kesehatan
Publisher : Universitas An Nasher - Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65099/t9ejkz89

Abstract

Background: Scoliosis is a spinal bone deformity characterized by lateral curvature of the vertebral column to the right or left. Long spine vertebrae radiography is a diagnostic examination to detect scoliosis. Purpose: To describe the application of stitching method in long spine vertebrae radiography examination for scoliosis cases at Waled Public Hospital. Method: A descriptive qualitative study on 3 scoliosis patients who underwent long spine vertebrae radiography examination at Waled Public Hospital from June 2024. Data were collected through observation and interviews. Result: The radiography technique used anteroposterior and lateral projections. The stitching method combines radiography results into a single image so the entire vertebrae can be seen. The image was able to assess vertebrae anatomy for scoliosis diagnosis. Conclusion: Long spine vertebrae radiography examination using the stitching method can display the entire vertebrae in a single image, making it easier for radiologists to see and assess vertebrae anatomy for scoliosis diagnosis.
Co-Authors Abdul Syukur Beni, Muhammad Imamul Janna, Ahmad Nurhindarto, Aris Sinaga, Sunita Soeleman, M. Arief