Detection of Tuberculosis Disease Using Image Processing Technique

Abstract

Machine learning is a branch of computing that studies the design of algorithms with the ability to “learn.” A subfield would be deep learning, which is a series of techniques that make use of deep artificial neural networks, that is, with more than one hidden layer, to computationally imitate the structure and functioning of the human organ and related diseases. The analysis of health interest images with deep learning is not limited to clinical diagnostic use. It can also, for example, facilitate surveillance of disease-carrying objects. There are other examples of recent efforts to use deep learning as a tool for diagnostic use. Chest X-rays are one approach to identify tuberculosis; by analysing the X-ray, you can spot any abnormalities. A method for detecting the presence of tuberculosis in medical X-ray imaging is provided in this paper. Three different classification methods were used to evaluate the method: support vector machines, logistic regression, and nearest neighbors. Cross-validation and the formation of training and test sets were the two classification scenarios used. The acquired results allow us to assess the method’s practicality.

Applying Dynamic Systems to Social Media by Using Controlling Stability

“…In the arrangement of values for a parameter k , [ 10 ] has proposed another 3D attractor that shows chaotic behavior in distinct respects and not diffeomorphic with Lorenz [ 11 – 18 ]. The first chaotic nonlinear system has been suggested by Lorenz [ 19 – 22 ] in which is a generalization of the Lorenz system. The Lorenz system's messy structure is utilized.…”

Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

“…Neural networks are adjusted for the diagnosis of diseases in agriculture since they are composed of numerous processing units, simulating the brain's functionality and the way of performing functions as a living being [ 19 ]. Neural networks have been frequently applied in various phytosanitary diagnosis applications, obtaining very favorable results and with a higher degree of certainty than other artificial intelligence techniques, which they provide in phytosanitary diagnoses [ 20 ].…”

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

“…From the experiment, they found that the hybrid model of CNN and SVM performs with an accuracy 98.49%. In the same concern, Alsaffar et al [11] evaluated several classification models including SVM, logistic regression, and nearest neighbors to analyze X-ray imaging to spot abnormalities. It was recorded that SVM achieved the best performance.…”