Implementation source code of Neural Computing and Applications (2023) 35:19279–19295 https://doi.org/10.1007/s00521-023-08717-4 ,- volV) (0123456789().,-volV)(0123456789().

Javeria Amin

Source Code for: Detection of Brain Space-Occupying Lesions Using Quantum Machine Learning", "description": "Source code for the four-phase deep-learning pipeline presented in Amin, J., Anjum, M. A., Gul, N., & Sharif, M. (2023), 'Detection of brain space-occupying lesions using quantum machine learning', Neural Computing and Applications, 35, 19279-19295 (https://doi.org/10.1007/s00521-023-08717-4). Phase I: 32-layer dlnetwork MRI denoiser. Phase II: J.Qnet Quanvolutional Neural Network for tumor/non-tumor slice classification. Phase III: ONNX-YOLOv2tiny model for tumor mass / edema localization. Phase IV: U-Net model for pixel-wise lesion segmentation. Evaluated on the BRATS 2020/2019/2012 challenge datasets and a locally collected 24-patient hospital dataset.", "creators": [ { "name": "Amin, Javaria", "affiliation": "Department of Computer Science, University of Wah, Wah Cantt, Pakistan" }, { "name": "Anjum, Muhammad Almas", "affiliation": "National University of Technology (NUTECH), Islamabad, Pakistan" }, { "name": "Gul, Nadia", "affiliation": "FCPS Diagnostic Radiology, Consultant Radiologist, POF Hospital and Associate Professor Radiology, Wah Medical College, Wah Cantt, Pakistan" }, { "name": "Sharif, Muhammad", "affiliation": "COMSATS University Islamabad, Wah Campus, Pakistan" } ], "upload_type": "software", "access_right": "open", "license": "MIT", "keywords": [ "brain tumor", "space-occupying lesion", "MRI", "deep learning", "image denoising", "quanvolutional neural network", "quantum machine learning", "J.Qnet", "object detection", "YOLOv2", "ONNX", "semantic segmentation", "U-Net", "BRATS", "MATLAB", "TensorFlow", "PennyLane" ], "related_identifiers": [ { "relation": "isSupplementTo", "identifier": "10.1007/s00521-023-08717-4", "scheme": "doi" } ], "notes": "Replace repository-code / add your Zenodo-minted DOI to CITATION.cff once this archive is published."}