Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy. (February 2022)

• Record Type: Journal Article
• Authors: Momeny, Mohammad; Neshat, Ali Asghar; Gholizadeh, Abdolmajid; Jafarnezhad, Ahad; Rahmanzadeh, Elham; Marhamati, Mahmoud; Moradi, Bagher; Ghafoorifar, Ali; Zhang, Yu-Dong
• Is Part Of: Computers in biology and medicine. Volume 141(2022)
• Publication Date: 2022-02
• Subjects: Greedy Autoaugment -- Tuberculosis -- Convolutional neural network -- Dropout -- Mixed pooling -- Deep learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105175
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Although tuberculosis (TB) is a disease whose cause, epidemiology and treatment are well known, some infected patients in many parts of the world are still not diagnosed by current methods, leading to further transmission in society. Creating an accurate image-based processing system for screening patients can help in the early diagnosis of this disease. We provided a dataset containing1078 confirmed negative and 469 positive Mycobacterium tuberculosis instances. An effective method using an improved and generalized convolutional neural network (CNN) was proposed for classifying TB bacteria in microscopic images. In the preprocessing phase, the insignificant parts of microscopic images are excluded with an efficient algorithm based on the square rough entropy (SRE) thresholding. Top 10 policies of data augmentation were selected with the proposed model based on the Greedy AutoAugment algorithm to resolve the overfitting problem. In order to improve the generalization of CNN, mixed pooling was used instead of baseline one. The results showed that employing generalized pooling, batch normalization, Dropout, and PReLU have improved the classification of Mycobacterium tuberculosis images. The output of classifiers such as Naïve Bayes-LBP, KNN-LBP, GBT-LBP, Naïve Bayes-HOG, KNN-HOG, SVM-HOG, GBT-HOG indicated that proposed CNN has the best results with an accuracy of 93.4%. The improvements of CNN based on the proposed model can yield promising results for diagnosing TB. Highlights: The unwanted parts of microscopic images captured by the camera are eliminated by the optimal image thresholding based on the square rough entropy (SRE). The overfitting problem is solved by dropout and a data augmentation method based on Greedy AutoAugment (GAA). The generalization of CNN for the classification of Mycobacterium tuberculosis is improved by using mixed pooling instead of baseline pooling. The proposed model improves fitting with approximately zero overhead loads via a novel parametric ReLU (PReLU). The best performance was obtained for the proposed method with an overall accuracy rate of 93.4%.