Alzheimer’s Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one’s ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer’s disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer’s disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen’s Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

The standard method for examining malarial disease is performed through the examination of blood smears under the microscope for parasite-infected red blood cells and this is done by qualified technicians. The inadequacy of this traditional method is enhanced using advanced computer vision and deep learning methods to automatically classify the malarial parasite from the microscope’s blood smear image as infected or uninfected. The primary challenge lies in the classification of malarial parasitizes from blood smear data which decide the overall accuracy. The proposed work uses a deep convolutional neural network (DCNN) to detect the malarial parasitizes from smear blood cell images. The primary focus of the work is to (i) compare the validation loss and accuracy by tuning the hyper-parameters in order to classify the images and (ii) compute the Kappa Coefficient and Matthew’s correlation coefficient. The efficiency of the proposed model is analyzed by comparing the optimizers (Adam and Adagrad) at various epochs. The overall accuracy of the proposed DCNN model reached 98.9% compared to the existing state-of-the-art models by focusing on hyper-parameter tuning.