CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK
Puppala Praneeth
(India)
Majety Sathvika
(India)
Vivek Kommareddy
(India)
Madala Sarath
(India)
Saran Mallela
(India)
Koneru Suvarna Vani
vanikonerusuvarna@gmail.comVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh (India)
Prasun Chkrabarti
Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh (India)
Abstract
In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology.
Keywords:
Parkinson’s disease, Deep Residual Convolutional Neural Network, deep learning, health control, DRCNNReferences
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Authors
Puppala PraneethIndia
Authors
Majety SathvikaIndia
Authors
Vivek KommareddyIndia
Authors
Madala SarathIndia
Authors
Saran MallelaIndia
Authors
Koneru Suvarna Vanivanikonerusuvarna@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh India
Authors
Prasun ChkrabartiVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh India
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