Conference Paper

Explainable AI for Medical Image Processing: A Study on MRI in Alzheimer’s Disease

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Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, such as graphic processing unit, Internet of Things, cloud computing, and the blockchain, in the new generation of big data and Industry 4.0. In this paper, we construct an extensive survey over the period 1961–2018 of AI and deep learning. The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI, from underlying mechanisms to practical applications, from fundamental algorithms to industrial achievements, from current status to future trends. Although there exist many issues toward AI, it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.
] Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L Rubin, and Bradley J Erickson. 2017. Deep learning for brain MRI segmentation: state of the art and future directions
  • Akkus
  • al Akkus
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
  • El-Sappagh
  • al El-Sappagh
] Md Sarwar Kamal, Aden Northcote, Linkon Chowdhury, Nilanjan Dey, Rubén González Crespo, and Enrique Herrera-Viedma. 2021. Alzheimer’s patient analysis using image and gene expression data and explainable-AI to present associated genes
  • al Kamal
] Naimul Mefraz Khan, Nabila Abraham, and Marcia Hon. 2019. Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease
  • al Khan
] Samantha McGirr, Courtney Venegas, and Arun Swaminathan. 2020. Alzheimers disease: A brief review
  • al McGirr
] Muhammed Talo, Ulas Baran Baloglu, Özal Yıldırım, and U Rajendra Acharya. 2019. Application of deep transfer learning for automated brain abnormality classification using MR images
  • al Talo
] Fouzia Altaf, Syed MS Islam, Naveed Akhtar, and Naeem Khalid Janjua. 2019. Going deep in medical image analysis: concepts, methods, challenges, and future directions
  • al Altaf
] Ronald Carl Petersen, Paul S Aisen, Laurel A Beckett, Michael C Donohue, Anthony Collins Gamst, Danielle J Harvey, Clifford R Jack, William J Jagust, Leslie M Shaw, Arthur W Toga, 2010. Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization
  • al Petersen
] Boo-Kyeong Choi, Nuwan Madusanka, Heung-Kook Choi, Jae-Hong So, Cho-Hee Kim, Hyeon-Gyun Park, Subrata Bhattacharjee, and Deekshitha Prakash. 2020. Convolutional neural network-based MR image analysis for Alzheimer’s disease classification
  • al Choi
] Jordan D Fuhrman, Naveena Gorre, Qiyuan Hu, Hui Li, Issam El Naqa, and Maryellen L Giger. 2022. A review of explainable and interpretable AI with applications in COVID-19 imaging
  • al Fuhrman