PARKINSONNET: CLASSIFICATION PARKINSON'S DISEASE MODEL BASED ON NOVEL DEEP LEARNING STRUCTURE

Year 2023, , 259 - 276, 31.12.2023

Saif Al-jumaili

https://doi.org/10.53600/ajesa.1382806

Abstract

Over the last few decades, neuroimaging, particularly magnetic resonance imaging (MRI), has played a significant sessional part in studying brain functions and diseases. MRI images, combined with unique ML approaches and developed tools during these years, have opened up new opportunities for diagnosing neurological illnesses. However, due to the apparent symptoms that are similar to each other, brain illnesses are regarded as difficult to precisely detect. This research examines a newly developed algorithm (ParkinsonNet) to classify Parkinson's disorder into two unique classes which are Control (healthy) and Parkinson's (PD), this method is one of the deep learning approaches, Convolutional neural networks (CNN). CNN is one way that may be used to classify a range of brain illnesses such as Parkinson's. We employed a freshly constructed CNN technique from scratch, and we got 97.9% accuracy which is considered outstanding compared with recently published articles using the same dataset

Keywords

Parkinson’s disease (PD), Convolutional Neural Network (CNN), MRI Image, Deep Learning.

References

• Aich, Satyabrata, Hee-Cheol Kim, Kueh Lee Hui, Ahmed Abdulhakim Al-Absi, and Mangal Sain. 2019. "A supervised machine learning approach using different feature selection techniques on voice datasets for prediction of Parkinson’s disease." In 2019 21st International Conference on Advanced Communication Technology (ICACT) ICACT), 1116 21. IEEE.
• Al-azzawi, Athar Hussein A li, Saif Al jumaili, Abdullahi Abdu Ibrahim, and Adil Deniz Duru. 2022. "Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on Fourier Transform." In AIP Conference Proceedings , 020003. AIP Publishing LLC.
• Al-Fatlawi, Ali H, Mohammed H Jabardi, and Sai Ho Ling. 2016. "Efficient diagnosis system for Parkinson's disease using deep belief network." In 2016 IEEE Congress on evolutionary computation (CEC) CEC), 1324 30. IEEE.
• Al-Jumaili, Saif, Athar Al Azzawi, Osman Nuri Uçan, and Adil Deniz Duru. 2023. 'Classification of the Level of Alzheimer's Disease Using Anatomical Magnetic Resonance Images Based on a Novel Deep Learning Structure.' in, Diagnosis of Neurological Disorders Based on Deep Learning Techniques (CRC press)
• Al-Jumaili, Saif, Ahmed Al Jumaili, Salam Alyassri, Adil Deniz Duru, and Osman Nuri Uçan. 2022. "Recent Advances on Convolutional Architectures in Medical Applications: Classical or Quantum?" In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) ISMSIT), 800 05. IEEE.
• Alharthi, Abdullah S, Alexander J Casson, and Krikor B Ozanyan. 2020. 'Gait spatiotemporal signal analysis for Parkinson’s disease detection and severity rating', IEEE Sensors Journal , 21: 1838 48.
• Ali, Hafsa Moontari, M Shamim Kaiser, and Mufti Mahmud. 2019. "Application of convolutional neural network in segmenting brain regions from MRI data." In International conference on brain informatics , 136 46. Springer.
• Ali, Liaqat, Shafqat Ullah Khan, Muhammad Arshad, Sardar Ali, and Muhammad Anwar. 2019. "A multi model framework for evaluating type of speech samples having complementary information about Parkinson's disease." In 2019 International conference on electrical, communication, and computer engineering (ICECCE) ICECCE), 1 5. IEEE.
• Ali, Liaqat, Ce Zhu, Noorbakhsh Amiri Golilarz, Ashir Javeed, Mingyi Zhou, and Yipeng Liu. 2019. 'Reliable Parkinson’s disease detection by analyzing handwritten drawings: construction of an unbiased cascaded learning system based on feature selection and adaptive boosting model', IEEE Access , 7: 116480 89.
• Alqahtani, Ebtesam J, Fatimah H Alshamrani, Hajra F Syed, and Sunday O Olatunji. 2018. "Classification of Parkinson’s disease using NNge classification algorithm." In 2018 21st Saudi Computer Society National Computer Conference (NCC) NCC), 1 7. IEEE.
• Anand, Anuj, Md Amaan Haque, John Sahaya Rani Alex, and Nithya Venkatesan. 2018. "Evaluation of Machine learning and Deep learning algorithms combined with dimentionality reduction techniques for classification of Parkinson’s Disease." In 2018 IEEE international symposium on signal processing and information technology (ISSPIT) ISSPIT), 342 47. IEEE.
• Buongiorno, Domenico, Ilaria Bortone, Giacomo Donato Cascarano, Gianpaolo Francesco Trotta, Antonio Brunetti, and Vitoantonio Bevilacqua. 2019. 'A low cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease', BMC Medical Informatics and Decision Making , 19: 1 13.
• Butt, Abdul Haleem, Filippo Cavallo, Carlo Maremmani, and Erika Rovini. 2020. "Biomechanical parameters assessment for the classification of Parkinson disease using bidirectional long short term memory." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) EMBC), 5761 64. IEEE.
• Cai, Zhennao, Jianhua Gu, Caiyun Wen, Dong Zhao, Chunyu Huang, Hui Huang, Changfei Tong, Jun Li, and Huiling Chen. 2018. 'An intelligent Parkinson’s disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach', Computational and Mathematical Methods in Medicine ,
• Cantürk, Ismail. 2021. 'Fuzzy recurrence plot based analysis of dynamic and static spiral tests of Parkinson’s disease patients', Neural Computing and Applications , 33: 349 60.
• Celik, Enes, and Sevinc Ilhan Omurca. 2019. "Improving Parkinson's disease diagnosis with machine learning methods." In 2019 Scientific Meeting on Electrical Electronics & Biomedical Engineering and Computer Science (EBBT) EBBT), 1 4. Ieee.
• Chakraborty, Sabyasachi, Satyabrata Aich, and Hee Cheol Kim. 2020. 'Detection of Parkinson’s disease from 3T T1 weighted MRI scans using 3D convolutional neural network', Diagnostics , 10:
• Chand, Ganesh B, Dominic B Dwyer, Guray Erus, Aristeidis Sotiras, Erdem Varol, Dhivya Srinivasan, Jimit Doshi, Raymond Pomponio, Alessandro Pigoni, and Paola Dazzan. 2020. 'Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learn ing', Brain , 143: 1027 38.
• Chien, Chung Yao, Szu Wei Hsu, Tsung Lin Lee, Pi Shan Sung, and Chou Ching Lin. 2020. 'Using artificial neural network to discriminate Parkinson’s disease from other Parkinsonisms by focusing on putamen of dopamine transporter SPECT images', Biomedicines , 9:
• Cigdem, Ozkan, and Hasan Demirel. 2018. 'Performance analysis of different classification algorithms using different feature selection methods on Parkinson's disease detection', Journal of neuroscience methods , 309: 81 90.
• Dai, Yin, Zheng Tang, and Yang Wang. 2019. 'Data driven intelligent diagnostics for Parkinson’s disease', IEEE Access , 7: 106941 50. Danielyan, Arman, and Henry A Nasrallah. 2009. 'Neurological disorders in schizophrenia', Psychiatric Clinics , 32: 719 57.
• Diaz, Moises, Miguel Angel Ferrer, Donato Impedovo, Giuseppe Pirlo, and Gennaro Vessio. 2019. 'Dynamically enhanced static handwriting representation for Parkinson’s disease detection', Pattern Recognition Letters , 128: 204 10.
• Diaz, Moises, Momina Moetesum, Imran Siddiqi, and Gennaro Vessio. 2021. 'Sequence based dynamic handwriting analysis for Parkinson’s disease detection with one dimensional convolutions and BiGRUs', Expert Systems with Applications , 168:
• Dinesh, Akshaya, and Jennifer He. 2017. "Using machine learning to diagnose Parkinson's disease from voice recordings." In 2017 IEEE MIT Undergraduate Research Technology Conference (URTC) URTC), 1 4. IEEE.
• Erdogdu Sakar, Betul, Gorkem Serbes, and C Okan Sakar. 2017. 'Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease', Plos one , 12:
• Fontana, Roberto, Mario Agostini, Emanuele Murana, Mufti Mahmud, Elena Scremin, Maria Rubega, Giovanni Sparacino, Stefano Vassanelli, and Cristina Fasolato. 2017. 'Early hippocampal hyperexcitability in PS2APP mice: role of mutant PS2 and APP', Neurobiology of aging , 50: 64 76.
• Gazda, Matej, Máté Hireš, and Peter Drotár. 2021. 'Multiple fine tuned convolutional neural networks for Parkinson’s disease diagnosis from offline handwriting', IEEE Transactions on Systems, Man, and Cybernetics: Systems , 52: 78 89.
• Gil Martín, Manuel, Juan Manuel Montero, and Rubén San Segundo. 2019. 'Parkinson’s disease detection from drawing movements using convolutional neural networks', Electronics , 8:
• Gottapu, Ram Deepak, and Cihan H Dagli. 2018. 'Analysis of Parkinson’s disease data', Procedia computer science , 140: 334 41.
• Gunduz, Hakan. 2019. 'Deep learning based Parkinson’s disease classification using vocal feature sets', IEEE Access , 7: 115540 51.
• Haq, Ejaz Ul, Huang Jianjun, Xu Huarong, Kang Li, and Lifen Weng. 2022. 'A hybrid approach based on deep cnn and machine learning classifiers for the tumor segmentation and classification in brain MRI', Computational and Mathematical Methods in Medicine ,
• Hsu, Shih Yen, Li Ren Yeh, Tai Been Chen, Wei Chang Du, Yung Hui Huang, Wen Hung Twan, Ming Chia Lin, Yun Hsuan Hsu, Yi Chen Wu, and Huei Yung Chen. 2020. 'Classification of the multiple stages of Parkinson’s Disease by a deep convolution neural network ba sed on 99mTc TRODAT 1 SPECT images', Molecules , 25:
• Huang, Lele, Dan Zhang, Jianling Ji, Yujie Wang, and Ruijun Zhang. 2021. 'Central retina changes in Parkinson’s disease: a systematic review and meta analysis', Journal of neurology : 1 9.
• Hughes, Andrew J, Susan E Daniel, Yoav Ben‐Shlomo, and Andrew J Lees. 2002. 'The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service', Brain , 125: 861 70.
• Islam, Jyoti, and Yanqing Zhang. 2018. 'Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks', Brain informatics , 5: 1 14. ———. 2020. 'GAN based synthetic brain PET image generation', Brain informatics , 7: 1 12.
• Islam, Mohammad S, Imtiaz Parvez, Hai Deng, and Parijat Goswami. 2014. "Performance comparison of heterogeneous classifiers for detection of Parkinson's disease using voice disorder (dysphonia)." In 2014 International Conference on Informatics, Electronics & Vision (ICIEV) ICIEV), 1 7. IEEE.
• Kabir, Farjana. 2022. 'Alzheimer Parkinson Diseases 3 Class', Farjana Kabir, Accessed 20 octoper 2023. https://www.kaggle.com/datasets/farjanakabirsamanta/alzheimer diseases 3 class/data
• Khan, Maryam Mahsal, Alexandre Mendes, and Stephan K Chalup. 2018. 'Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction', Plos one , 13:
• Kollias, Dimitrios, Athanasios Tagaris, Andreas Stafylopatis, Stefanos Kollias, and Georgios Tagaris. 2018. 'Deep neural architectures for prediction in healthcare', Complex & Intelligent Systems , 4: 119 31.
• Kumari, KH Vijaya, and Soubhagya Sankar Barpanda. 2023. 'Residual UNet with Dual Attention Anensemble residual UNet with dual attention for multi‐modal and multi‐class brain MRI segmentation', International Journal of Imaging Systems and Technology , 33: 644 58.
• Kuresan, Harisudha, Dhanalakshmi Samiappan, and Sam Masunda. 2019. 'Fusion of WPT and MFCC feature extraction in Parkinson’s disease diagnosis', Technology and Health Care , 27: 363 72.
• Lee, SiehYang, Meng Hsiang Chen, Pi Ling Chiang, Hsiu Ling Chen, Kun Hsien Chou, Yueh Cheng Chen, Chiun Chieh Yu, Nai Wen Tsai, Shau Hsuan Li, and Cheng Hsien Lu. 2018. 'Reduced gray matter volume and respiratory dysfunction in Parkinson’s disease: a voxe l based morphometry study', BMC neurology , 18: 1 8.
• Lei, Baiying, Yujia Zhao, Zhongwei Huang, Xiaoke Hao, Feng Zhou, Ahmed Elazab, Jing Qin, and Haijun Lei. 2020. 'Adaptive sparse learning using multi template for neurodegenerative disease diagnosis', Medical Image Analysis , 61:
• Leparulo, Alessandro, Mufti Mahmud, Elena Scremin, Tullio Pozzan, Stefano Vassanelli, and Cristina Fasolato. 2019. 'Dampened slow oscillation connectivity anticipates amyloid deposition in the PS2APP mouse model of Alzheimer’s disease', Cells , 9:
• Litjens, Geert, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. 2017. 'A survey on deep learning in medical image analysis', Medical Image Analysis , 42: 60 88.
• Magesh, Pavan Rajkumar, Richard Delwin Myloth, and Rijo Jackson Tom. 2020. 'An explainable machine learning model for early detection of Parkinson's disease using LIME on DaTSCAN imagery', Computers in biology and medicine , 126:
• Mahmoudi, Tahereh, Zahra Mousavi Kouzahkanan, Amir Reza Radmard, Raheleh Kafieh, Aneseh Salehnia, Amir H Davarpanah, Hossein Arabalibeik, and Alireza Ahmadian. 2022. 'Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT ima ges using deep convolutional neural networks and texture descriptors', Scientific Reports , 12:
• Mahmud, Mufti, M Shamim Kaiser, T Martin McGinnity, and Amir Hussain. 2021. 'Deep learning in mining biological data', Cognitive computation , 13: 1 33.
• Mahmud, Mufti, Mohammed Shamim Kaiser, Amir Hussain, and Stefano Vassanelli. 2018. 'Applications of deep learning and reinforcement learning to biological data', IEEE transactions on neural networks and learning systems , 29: 2063 79.
• Mahmud, Mufti, and Stefano Vassanelli. 2016. 'Processing and analysis of multichannel extracellular neuronal signals: state of the art and challenges', Frontiers in neuroscience , 10: ———. 2019. 'Open source tools for processing and analysis of in vitro extracellular neuronal signals', In Vitro Neuronal Networks: From Culturing Methods to Neuro Technological Applications : 233 50.
• Marar, Shreerag, Debabrata Swain, Vivek Hiwarkar, Nikhil Motwani, and Akshar Awari. 2018. "Predicting the occurrence of Parkinson’s Disease using various Classification Models." In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT) ICACAT), 1 5. IEEE.
• Martinez Murcia, Francisco J, Juan M Górriz, Javier Ramírez, and Andres Ortiz. 2018. 'Convolutional neural networks for neuroimaging in Parkinson’s disease: Is preprocessing needed?', International journal of neural systems , 28:
• Mathew, Nimmy Ann, RS Vivek, and PR Anurenjan. 2018. "Early diagnosis of Alzheimer's disease from MRI images using PNN." In 2018 international CET conference on control, communication, and computing (IC4) IC4), 161 64. IEEE.
• Miah, Yunus, Chowdhury Nazia Enam Prima, Sharmeen Jahan Seema, Mufti Mahmud, and M Shamim Kaiser. 2021. "Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets." In Advances on Smart and Soft Computing: Proceedings of ICACIn 2020 , 79 89. Springer.
• Moharkan, Zaid Ajaz, Harshul Garg, Tanupriya Chodhury, and Praveen Kumar. 2017. "A classification based Parkinson detection system." In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon) SmartTechCon), 1509 13. IEEE.
• Montaña, David, Yolanda Campos Roca, and Carlos J Pérez. 2018. 'A Diadochokinesis based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease', Computer Methods and Programs in Biomedicine , 154: 89 97.
• Moujahid, Hicham, Bouchaib Cherradi, and Lhoussain Bahatti. 2020. "Convolutional neural networks for multimodal brain MRI images segmentation: A comparative study." In Smart Applications and Data Analysis: Third International Conference, SADASC 2020, Marrakesh, Morocco, June 25 26, 2020, Proceedings 3 , 329 38. Springer.
• Neromyliotis, Eleftherios, Theodosis Kalamatianos, Athanasios Paschalis, Spyridon Komaitis, Konstantinos N Fountas, Eftychia Z Kapsalaki, George Stranjalis, and Ioannis Tsougos. 2022. 'Machine learning in meningioma MRI: past to present. A narrative review ', Journal of Magnetic Resonance Imaging , 55: 48 60.
• Nõmm, Sven, Sergei Zarembo, Kadri Medijainen, Pille Taba, and Aaro Toomela. 2020. 'Deep CNN based classification of the archimedes spiral drawing tests to support diagnostics of the Parkinson’s disease', IFAC PapersOnLine , 53: 260 64.
• Noor, Manan Binth Taj, Nusrat Zerin Zenia, M Shamim Kaiser, Mufti Mahmud, and Shamim Al Mamun. 2019. "Detecting neurodegenerative disease from MRI: a brief review on a deep learning perspective." In Brain Informatics: 12th International Conference, BI 2019, Haikou, China, December 13 15, 2019, Proceedings 12 , 115 25. Springer.
• Norwitz, Nicholas G, David J Dearlove, Meng Lu, Kieran Clarke, Helen Dawes, and Michele T Hu. 2020. 'A ketone ester drink enhances endurance exercise performance in Parkinson’s disease', Frontiers in neuroscience , 14:
• Oh, Shu Lih, Yuki Hagiwara, U Raghavendra, Rajamanickam Yuvaraj, N Arunkumar, M Murugappan, and U Rajendra Acharya. 2020. 'A deep learning approach for Parkinson’s disease diagnosis from EEG signals', Neural Computing and Applications , 32: 10927 33.
• Ortiz, Andrés, Jorge Munilla, Manuel Martínez Ibañez, Juan M Górriz, Javier Ramírez, and Diego Salas Gonzalez. 2019. 'Parkinson's disease detection using isosurfaces based features and convolutional neural networks', Frontiers in neuroinformatics , 13:
• Pereira, Clayton R, Danilo R Pereira, Gustavo H Rosa, Victor HC Albuquerque, Silke AT Weber, Christian Hook, and João P Papa. 2018. 'Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification ', Artificial intelligence in medicine , 87: 67 77.
• Piccardo, Arnoldo, Roberto Cappuccio, Gianluca Bottoni, Diego Cecchin, Luca Mazzella, Alessio Cirone, Sergio Righi, Martina Ugolini, Pietro Bianchi, and Pietro Bertolaccini. 2021. 'The role of the deep convolutional neural network as an aid to interpreting brain [18 F] DOPA PET/CT in the diagnosis of Parkinson’s disease', European radiology , 31: 7003 11.
• Poldrack, Russell A, Krzysztof J Gorgolewski, and Gaël Varoquaux. 2019. 'Computational and informatic advances for reproducible data analysis in neuroimaging', Annual Review of Biomedical Data Science , 2: 119 38.
• Prince, John, and Maarten De Vos. 2018. "A deep learning framework for the remote detection of Parkinson’s disease using smart phone sensor data." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) EMBC), 3144 47. IEEE.
• Ribeiro, Luiz CF, Luis CS Afonso, and Joao P Papa. 2019. 'Bag of samplings for computer assisted Parkinson's disease diagnosis based on recurrent neural networks', Computers in biology and medicine , 115:
• Salvatore, Christian, Antonio Cerasa, Isabella Castiglioni, F Gallivanone, A Augimeri, M Lopez, G Arabia, M Morelli, MC Gilardi, and A Quattrone. 2014. 'Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy', Journal of neuroscience methods , 222: 230 37.
• Senturk, Zehra Karapinar. 2020. 'Early diagnosis of Parkinson’s disease using machine learning algorithms', Medical Hypotheses , 138:
• Shatte, Adrian BR, Delyse M Hutchinson, and Samantha J Teague. 2019. 'Machine learning in mental health: a scoping review of methods and applications', Psychological medicine , 49: 1426 48.
• Sheibani, Razieh, Elham Nikookar, and Seyed Enayatollah Alavi. 2019. 'An ensemble method for diagnosis of Parkinson's disease based on voice measurements', Journal of medical signals and sensors , 9:
• Shen, Lu, Jun Shi, Bangming Gong, Yingchun Zhang, Yun Dong, Qi Zhang, and Hedi An. 2018. "Multiple empirical kernel mapping based broad learning system for classification of Parkinson’s disease with transcranial sonography." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) EMBC), 3132 35. IEEE.
• Shinde, Sumeet, Shweta Prasad, Yash Saboo, Rishabh Kaushick, Jitender Saini, Pramod Kumar Pal, and Madhura Ingalhalikar. 2019. 'Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI', NeuroImage: Clinical , 22:
• Stoessl, A Jon, Stephane Lehericy, and Antonio P Strafella. 2014. 'Imaging insights into basal ganglia function, Parkinson's disease, and dystonia', The Lancet , 384: 532 44.
• Sztahó, Dávid, Miklós Gábriel Tulics, Klára Vicsi, and István Valálik. 2017. "Automatic estimation of severity of Parkinson's disease based on speech rhythm related features." In 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) CogInfoCom), 000011 16. IEEE.
• Thaha, M Mohammed, K Pradeep Mohan Kumar, BS Murugan, S Dhanasekeran, P Vijayakarthick, and A Senthil Selvi. 2019. 'Brain tumor segmentation using convolutional neural networks in MRI images', Journal of medical systems , 43: 1 10.
• Tolosa, Eduardo, Gregor Wenning, and Werner Poewe. 2006. 'The diagnosis of Parkinson's disease', The Lancet Neurology , 5: 75 86.
• Tremblay, Cécilia, Jie Mei, and Johannes Frasnelli. 2020. 'Olfactory bulb surroundings can help to distinguish Parkinson’s disease from non parkinsonian olfactory dysfunction', NeuroImage: Clinical , 28:
• Wodzinski, Marek, Andrzej Skalski, Daria Hemmerling, Juan Rafael Orozco Arroyave, and Elmar Nöth. 2019. "Deep learning approach to Parkinson’s disease detection using voice recordings and convolutional neural network dedicated to image classification." In 2019 41st annual of the IEEE engineering in medicine and biology society (EMBC) EMBC), 71720. IEEE.
• Xiao, Bin, Naying He, Qian Wang, Zenghui Cheng, Yining Jiao, E Mark Haacke, Fuhua Yan, and Feng Shi. 2019. 'Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease', NeuroImage: Clinical , 24:
• Yaman, Orhan, Fatih Ertam, and Turker Tuncer. 2020. 'Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features', Medical Hypotheses , 135:
• Yamashita, Rikiya, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi. 2018. 'Convolutional neural networks: an overview and application in radiology', Insights into imaging , 9: 611 29.
• Yasaka, Koichiro, Koji Kamagata, Takashi Ogawa, Taku Hatano, Haruka Takeshige Amano, Kotaro Ogaki, Christina Andica, Hiroyuki Akai, Akira Kunimatsu, and Wataru Uchida. 2021.
• 'Parkinson’s disease: Deep learning with a parameter weighted structural connectom e matrix for diagnosis and neural circuit disorder investigation', Neuroradiology : 1 12.
• Zhang, Hanrui, Kaiwen Deng, Hongyang Li, Roger L Albin, and Yuanfang Guan. 2020. 'Deep learning identifies digital biomarkers for self reported Parkinson's disease', Patterns ,1.