A Review on Pneumonia Detection Using Image Processing Techniques

Due to their environment and way of life, people today experience various diseases. So, it is critical to identify issues at an early stage. However, it is challenging for a doctor to make an exact diagnosis based only on symptoms. Accurate diagnosis of a problem is the most challenging task to complete. The key to resolving this problem is to foresee the illness using data mining. The amount of data in the medical sciences is expanding quickly every year. Early patient treatment has profited from the proliferation of data in the healthcare and medical industries, boosting the accuracy of medical data analysis. Using illness data, data mining uncovers hidden patterns in a large amount of medical data. The detection and diagnosis of pneumonia are becoming increasingly automated. As we can see, the medical business uses and tests a range of technologies, implying that the detection of pneumonia will probably become less complicated. The approaches for detecting pneumonia using Support Vector Machine(SVM), Image Segmentation, Image Matching-SURF, and Convolutional Neural Networks(CNN) are evaluated in this paper. Pneumonia detection algorithms offer several features that ask for user input before delivering findings.