Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images
作者机构：Department of Biomedical EngineeringNear East UniversityNicosiaMersin 10Turkey Research Center for Al and loTFaculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept.Al and Robotics InstituteNear East UniversityMersin 1Turkey
出 版 物：《Data Intelligence》 (数据智能（英文）)
年 卷 期：2023年第5卷第4期
页 面：1008-1032页
核心收录：
学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1002[医学-临床医学] 081203[工学-计算机应用技术] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0835[工学-软件工程] 0802[工学-机械工程] 0836[工学-生物工程] 0803[工学-光学工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学]
主 题：Tuberculosis Deep Learning Pretrained AlexNet Chest X-ray Microscopic slide
摘 要：Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis *** can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to *** challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high *** this paper,we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet *** study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using X-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.
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