Classification of Parkinson's disease in brain MRI images using Deep Residual Convolutional Neural Network (DRCNN)

• Autorzy: Praneeth Puppala , Sathvika Majety , Kommareddy Vivek , Sarath Madala , Mallela Saran , Vani K. Suvarna , Chkrabarti Prasun

Identyfikatory

DOI

10.35784/acs-2023-19

• Języki publikacji: EN

Abstrakty

EN

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, the authors propose a technique to classify Parkinson’s disease by MRI brain images. Initially, the input data is normalized using the min-max normalization method, and then noise is removed from the input images using a median filter. The Binary Dragonfly algorithm is then used to select features. In addition, the Dense-UNet technique is used to segment the diseased part from brain MRI images. The disease is then classified as Parkinson's disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with the Enhanced Whale Optimization Algorithm (EWOA) to achieve better classification accuracy. In this work, the Parkinson's Progression Marker Initiative (PPMI) public dataset for Parkinson's MRI images is used. Indicators of accuracy, sensitivity, specificity and precision are used with manually collected data to evaluate the effectiveness of the proposed methodology.

Słowa kluczowe

EN

Parkinson’s disease; Deep Residual Convolutional Neural Network; deep learning; health control

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 2
• Strony: 125--146
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Praneeth Puppala

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

• https://orcid.org/0009-0008-1230-9562

autor

Sathvika Majety

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

• https://orcid.org/0009-0009-3380-3137

autor

Kommareddy Vivek

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

• https://orcid.org/0009-0006-8417-7058

autor

Sarath Madala

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

• https://orcid.org/0009-0008-1258-3828

autor

Mallela Saran

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

• https://orcid.org/0009-0004-4468-6136

autor

Vani K. Suvarna

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

• https://orcid.org/0000-0003-1899-9580

autor

Chkrabarti Prasun

• ITM SLS Baroda University, Vadodara

• https://orcid.org/0009-0002-9892-7754

Bibliografia

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