Pneumonia Diagnosis through pixels -- A Deep Learning Model for detection and classification

Manual identification and classification of pneumonia and COVID-19 infection is a cumbersome process that, if delayed can cause irreversible damage to the patient. We have compiled CT scan images from various sources, namely, from the China Consortium of Chest CT Image Investigation (CC-CCII), the Negin Radiology located at Sari in Iran, an open access COVID-19 repository from Havard dataverse, and Sri Ramachandra University, Chennai, India. The images were preprocessed using various methods such as normalization, sharpening, median filter application, binarizing, and cropping to ensure uniformity while training the models. We present an ensemble classification approach using deep learning and machine learning methods to classify patients with the said diseases. Our ensemble model uses pre-trained networks such as ResNet-18 and ResNet-50 for classification and MobileNetV2 for feature extraction. The features from MobileNetV2 are used by the gradient-boosting classifier for the classification of patients. Using ResNet-18, ResNet-50, and the MobileNetV2 aided gradient boosting classifier, we propose an ensemble model with an accuracy of 98 percent on unseen data.

PPG Signals for Hypertension Diagnosis: A Novel Method using Deep Learning Models

Hypertension is a medical condition characterized by high blood pressure, and classifying it into its various stages is crucial to managing the disease. In this project, a novel method is proposed for classifying stages of hypertension using Photoplethysmography (PPG) signals and deep learning models, namely AvgPool_VGG-16. The PPG signal is a non-invasive method of measuring blood pressure through the use of light sensors that measure the changes in blood volume in the microvasculature of tissues. PPG images from the publicly available blood pressure classification dataset were used to train the model. Multiclass classification for various PPG stages were done. The results show the proposed method achieves high accuracy in classifying hypertension stages, demonstrating the potential of PPG signals and deep learning models in hypertension diagnosis and management.