Application of Improved Support Vector Machine for Pulmonary Syndrome Exposure with Computer Vision Measures

Abstract

Background: In many medically developed applications, the process of early diagnosis in cases of pulmonary disease does not exist. Many people experience immediate suffering due to the lack of early diagnosis, even after becoming aware of breathing difficulties in daily life. Because of this, identifying such hazardous diseases is crucial, and the suggested solution combines computer vision and communication processing techniques. As computing technology advances, a more sophisticated mechanism is required for decision-making. Objective: The major objective of the proposed method is to use image processing to demonstrate computer vision-based experimentation for identifying lung illness. In order to characterize all the uncertainties that are present in nodule segments, an improved support vector machine is also integrated into the decision-making process. Methods: As a result, the suggested method incorporates an Improved Support Vector Machine (ISVM) with a clear correlation between various margins. Additionally, an image processing technique is introduced where all impacted sites are marked at high intensity to detect the presence of pulmonary syndrome. Contrary to other methods, the suggested method divides the image processing methodology into groups, making the loop generation process much simpler. Results: Five situations are taken into account to demonstrate the effectiveness of the suggested technique, and test results are compared with those from existing models. Conclusion: The proposed technique with ISVM produces 83 percent of successful results.

Shitharth Selvarajan

Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.