Lung Infection and Identification using Heatmap

Lung disease identification using heatmap is an automated diagnosis system that utilizes the visualization of heatmaps to identify and classify lung diseases from chest X- Radiation images. The system applies a deep learning-based approach to automatically extract and learn discriminative features from the input images, which are then used to generate heatmaps highlighting the regions of the lung that are affected by the disease. The heatmaps provide an intuitive visualization of the disease, which can be used to aid radiologists in making accurate diagnoses. The approach has the potential to increase the efficiency and accuracy of clinical diagnosis and has been proven to achieve high accuracy in the identification and categorization of a variety of lung infection, including pneumonia and Novel coronavirus. Lung diseases have become a major health concern worldwide, causing significant morbidity and mortality. Early identification and timely treatment of these diseases can significantly improve patient outcomes. This research paper, proposes a novel approach to identify lung diseases using heatmap analysis. CXR of patients was collected with various lung infection, including pneumonia and novel coronavirus. The images were pre-processed to enhance the features and reduce noise. A heatmap analysis technique was applied to these images to generate heatmaps that highlight the regions of the lung that are most affected by the disease. A deep learning model was then used to classify diseases using the heatmaps. The pictures were categorized into several types of lung infection groups using a convolutional neural network (CNN). The CNN obtained good illness classification accuracy after being trained on a huge dataset of CXR. The proposed approach was evaluated on a dataset of 317 CXR. The findings indicated that our method classified diseases with an overall accuracy of 98.55%. The suggested method may increase the precision and efficiency of diagnosing lung diseases. The heatmap analysis technique can help clinicians identify the regions of the lung that are most affected by the disease, which can aid in diagnosis and treatment planning. Furthermore, the deep learning model can be trained on large datasets to improve its accuracy and robustness.