Ambresh Bhadrashetty

Work place: Department of CSE (MCA), Visvesvaraya Technological University, Centre for PG Studies, Kalaburagi, India



Research Interests: Data Structures and Algorithms, Data Mining, Computational Learning Theory, Artificial Intelligence


Ambresh Bhadrashetty, (Ph.D.), is working as Assistant Professor at the Department of Computer Science and Engineering (MCA), Visvesvaraya Technological University, Center for PG Studies, Kalaburagi, India. Research areas are Machine Learning, Data Mining, and Artificial Intelligence. He has published more than 15 peer-reviewed research articles.

Author Articles
Detection and Classification of Alzheimer’s Disease by Employing CNN

By Smt. Swaroopa Shastri Ambresh Bhadrashetty Supriya Kulkarni

DOI:, Pub. Date: 8 Apr. 2023

Alzheimer’s illness is an ailment of mind which results in mental confusion, forgetfulness and many other mental problems. It effects physical health of a person too. When treating a patient with Alzheimer's disease, a proper diagnosis is crucial, especially into earlier phases of condition as when patients are informed of the risk of the disease, they can take preventative steps before irreparable brain damage occurs. The majority of machine detection techniques are constrained by congenital (present at birth) data, however numerous recent studies have used computers for Alzheimer's disease diagnosis. The first stages of Alzheimer's disease can be diagnosed, but illness itself cannot be predicted since prediction is only helpful before it really manifests. Alzheimer’s has high risk symptoms that effects both physical and mental health of a patient. Risks include confusion, concentration difficulties and much more, so with such symptoms it becomes important to detect this disease at its early stages. Significance of detecting this disease is the patient gets a better chance of treatment and medication. Hence our research helps to detect the disease at its early stages. Particularly when used with brain MRI scans, deep learning has emerged as a popular tool for the early identification of AD. Here we are using a 12- layer CNN that has the layers four convolutional, two pooling, two flatten, one dense and three activation functions. As CNN is well-known for pattern detection and image processing, here, accuracy of our model is 97.80%.

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Early Detection of Dementia using Deep Learning and Image Processing

By Basavaraj Mali Patil Megha Rani Raigonda Sudhir Anakal Ambresh Bhadrashetty

DOI:, Pub. Date: 8 Feb. 2023

Dementia is the world's most deadly disease. A degenerative disorder that affects the thinking, memory, and communication abilities of the human brain. According to World Health Organization, more than 40 million people worldwide suffer from this illness. One of the most common methods for analyzing the human brain, including detecting dementia, is using MRI (Magnetic resonance imaging) data, which provides insight into the inner working of the human body. Using MRI images a deep Convolution neural network was designed to detect dementia, we are utilizing image processing to help doctors detect diseases and make decisions on observation, in an earlier stage of the disease. In this paper, we are going to get to the bottom of the DenseNet-169 model, to detect Dementia. There are approximately 6000 brain MRI images in the database for which the DenseNet-169 model has been used for classification purposes. It is a Convolution Neural Network (CNN) model that classifies Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia. The denseNet-169 model helps us determine Dementia disease. And also present the 97% accuracy for clarification of disease is present in the patient body. we are conducted this survey for providing effective disease prediction model for physicians to conclude that the disease stage is accurate and provide proper treatment for that.  

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