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This paper presents a new method to identify the types of noise present in microarray images based on Convolution Neural Network known as CNN-INC. It does not demand any pre-processing of noisy images except resizing if needed. The size of dataset and the parameters of training options are selected to achieve 100% test accuracy (Zero ERROR).The CNN training speed is maintained substantially higher without compromising on accuracy. Experimental analysis shows that the proposed algorithm gives promising results compared to existing methods.

Alzheimer's disease is a degenerative disease in which brain cells die and deteriorate. It is the most prevalent reason for dementia, which is defined as a progressive decrease in thinking, conduct, and social skills that impairs a person's capacity to operate independently. Although it is fatal the early diagnosis of Alzheimer's can be extremely helpful. Our main aim is to help with the diagnosis of this disease in its early stages using the VGG16 classifier which is a convolutional neural network (CNN) that is 16 layers deep. The dataset consists of MRI images of the brain. Data augmentation is done to significantly increase the diversity of data available and Data pre-processing helps to enhance the overall truthfulness of the proposed approach.