Diabetic Retinopathy (DR) is a serious hazard that can result in irreversible blindness if not addressed in a timely manner. Hence, numerous techniques have been proposed for the accurate and timely detection of this disease. Out of these, Deep Learning (DL) and Computer Vision (CV) methods for multiclass categorization of color fundus images diagnosed with Diabetic Retinopathy have sparked considerable attention. In this paper, we attempt to develop an extended ResNet152V2 architecture-based Deep Learning model, named ResNet2.0 to aid the timely detection of DR. The APTOS-2019 dataset was used to train the model. This consists of 3662 fundus images belonging to five different stages of DR: no DR (Class 0), mild DR (Class 1), moderate DR (Class 2), severe DR (Class 3), and proliferative DR (Class 4). The model was gauged based on ability to detect stage-wise DR. The images were pre-processed using negative and positive weighted Gaussian-based masks as feature engineering to further enhance the quality of the fundus images by removing the noise and normalizing the images. Up-sampling and data augmentation methods were used to address the skewness of the original dataset. The proposed model achieved an overall accuracy of 91% and an area under the receiver-operating characteristic curve (AUC) score of 95.1%, outperforming existing Deep Learning models by around 10%. Furthermore, the class-wise F1 score for No DR was 92%, Mild DR was 82%, Moderate DR was 66%, Severe was DR 89% and Proliferative DR was 80%.

A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In order to achieve this, the paper proposes a model that uses an extended deep Convolutional Neural Network (CNN) named Contour Extraction based Extended EfficientNet-B0 (CE-EEN-B0) which is a feed-forward neural network with the efficient net layers; three convolutional layers and max-pooling layers; and finally, the global average pooling layer. The site of tumors in the brain is one feature that determines its effect on the functioning of an individual. Thus, this CNN architecture classifies brain tumors into four categories: No tumor, Pituitary tumor, Meningioma tumor, and Glioma tumor. This network provides an accuracy of 97.24%, a precision of 96.65%, and an F1 score of 96.86% which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image. This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.