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.com
Velagapudi 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, DRCNN

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Published
2023-06-30

Cited by

Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Vani, K. S., & Chkrabarti, P. (2023). CLASSIFICATION OF PARKINSON’S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK. Applied Computer Science, 19(2), 125–146. https://doi.org/10.35784/acs-2023-19

Authors

Puppala Praneeth 

India

Authors

Majety Sathvika 

India

Authors

Vivek Kommareddy 

India

Authors

Madala Sarath 

India

Authors

Saran Mallela 

India

Authors

Koneru Suvarna Vani 
vanikonerusuvarna@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh India

Authors

Prasun Chkrabarti 

Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh India

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