Ischemia and Hemorrhage detection in CT images with Hyper parameter optimization of classification models and Improved UNet Segmentation Model

Deep learning is a powerful technique that has been applied to the task of stroke detection using medical imaging. Stroke is a medical condition that occurs when the blood supply to the brain is interrupted, which can cause brain damage and other serious complications. Detection of stroke is important in order to minimize damage and improve patient outcomes. One of the most common imaging modalities used for stroke detection is CT(Computed Tomography). CT can provide detailed images of the brain and can be used to identify the presence and location of a stroke. Deep learning models, particularly convolutional neural networks (CNNs), have shown promise for the task of stroke detection using CT images. These models can learn to automatically identify patterns in the images that are indicative of a stroke, such as the presence of an infarct or hemorrhage. Some examples of deep learning models used for stroke detection in CT images are U-Net, which is commonly used for medical image segmentation tasks, and CNNs, which have been trained to classify brain CT images into normal or abnormal. The purpose of this study is to identify the type of stroke from brain CT images taken without the administration of a contrast agent, i.e. occlusive (ischemic) or hemorrhagic (hemorrhagic). Stroke images were collected and a dataset was constructed with medical specialists. Deep learning classification models were evaluated with hyperparameter optimization techniques. And the result segmented with improved Unet model to visualize the stroke in CT images. Classification models were compared and VGG16 achieved %94 success. Unet model was achieved %60 IOU and detected the ischemia and hemorrhage differences.

Keywords:

deep learning, segmentation hyperparameter, unet,

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[1] Dong Chuang Guo; Jun Gu; Jian He; Hai Rui Chu; Na Dong; Yi Feng Zheng; "External Validation Study on The Value of Deep Learning Algorithm for The Prediction of Hematoma Expansion from Noncontrast CT Scans", Bmc Medical Imaging, 2022.
[2] Md Moniruzzaman Emon; Tareque Rahman Ornob; Moqsadur Rahman; "Classifications of Skull Fractures Using CT Scan Images Via CNN with Lazy Learning Approach", Arxiv-Eess.Iv, 2022.
[3] Miguel López-Pérez; Arne Schmidt; Yunan Wu; Rafael Molina; Aggelos K Katsaggelos; "Deep Gaussian Processes for Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection", Computer Methods And Programs In Biomedicine, 2022.
[4] V Pandimurugan; S Rajasoundaran; Sidheswar Routray; A V Prabu; Hashem Alyami; Abdullah Alharbi; Sultan Ahmad; "Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme", Computational Intelligence And Neuroscience, 2022.
[5] Farhan Ullah; Jihoon Moon; Hamad Naeem; Sohail Jabbar; "Explainable Artificial Intelligence Approach in Combating Real-time Surveillance of COVID19 Pandemic from CT Scan and X-ray Images Using Ensemble Model", The Journal Of Supercomputing, 2022.
[6] Murugan Hemalatha; "A Hybrid Random Forest Deep Learning Classifier Empowered Edge Cloud Architecture for COVID-19 and Pneumonia Detection", Expert Systems With Applications, 2022.
[7] Nirmala Devi Kathamuthu; Shanthi Subramaniam; Quynh Hoang Le; Suresh Muthusamy; Hitesh Panchal; Suma Christal Mary Sundararajan; Ali Jawad Alrubaie; Musaddak Maher Abdul Zahra; "A Deep Transfer Learning-based Convolution Neural Network Model for COVID-19 Detection Using Computed Tomography Scan Images for Medical Applications", Advances In Engineering Software (Barking, London, England ..., 2022.
[8] Yue Zhao; Zhongyang Wang; Xinyao Liu; Qi Chen; Chuangang Li; Hongshuo Zhao; Zhiqiong Wang; "Pulmonary Nodule Detection Based on Multiscale Feature Fusion", Computational And Mathematical Methods In Medicine, 2022.
[9] Jing Xu; Haojie Ren; Shenzhou Cai; Xiaoping Zhang; "An Improved Faster R-CNN Algorithm for Assisted Detection of Lung Nodules", Computers In Biology And Medicine, 2022.
[10] Yashwanth Manjunatha; Vanshali Sharma; Yuji Iwahori; M K Bhuyan; Aili Wang; Akira Ouchi; Yasuhiro Shimizu; "Lymph Node Detection in CT Scans Using Modified U-Net with Residual Learning and 3D Deep Network", International Journal Of Computer Assisted Radiology And ..., 2023.
[11] Ujjwal Upadhyay; Mukul Ranjan; Satish Golla; Swetha Tanamala; Preetham Sreenivas; Sasank Chilamkurthy; Jeyaraj Pandian; Jason Tarpley; "Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement", Arxiv-Eess.Iv, 2022.
[12] Yang Wang; Junkai Zhu; Jinli Zhao; Wenyi Li; Xin Zhang; Xiaolin Meng; Taige Chen; Ming Li; Meiping Ye; Renfang Hu; Shidan Dou; Huayin Hao; Xiaofen Zhao; Xiaoming Wu; Wei Hu; Cheng Li; Xiaole Fan; Liyun Jiang; Xiaofan Lu; Fangrong Yan; "Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability", Frontiers In Neurology, 2022.
[13] John T Murchison; Gillian Ritchie; David Senyszak; Jeroen H Nijwening; Gerben van Veenendaal; Joris Wakkie; Edwin J R van Beek; "Validation of A Deep Learning Computer Aided System for CT Based Lung Nodule Detection, Classification, and Growth Rate Estimation in A Routine Clinical Population", Plos One, 2022.
[14] Ine Dirks; Marleen Keyaerts; Bart Neyns; Jef Vandemeulebroucke; "Computer-aided Detection and Segmentation of Malignant Melanoma Lesions on Whole-body 18 F-FDG PET/CT Using An Interpretable Deep Learning Approach", Computer Methods And Programs In Biomedicine, 2022.
[15] Jake Kendrick; Roslyn J Francis; Ghulam Mubashar Hassan; Pejman Rowshanfarzad; Jeremy S L Ong; Martin A Ebert; "Fully Automatic Prognostic Biomarker Extraction from Metastatic Prostate Lesion Segmentations in Whole-body [ 68 Ga]Ga-PSMA-11 PET/CT Images", European Journal Of Nuclear Medicine And Molecular Imaging, 2022.
[16] Chetna Kaushal; Md Khairul Islam; Sara A Althubiti; Fayadh Alenezi; Romany F Mansour; "A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks", Computational Intelligence And Neuroscience, 2022.
[17] T Ahila; A C Subhajini; "E-GCS: Detection of COVID-19 Through Classification By Attention Bottleneck Residual Network", Engineering Applications Of Artificial Intelligence, 2022.
[18] Xun Wang; Hanlin Li; Pan Zheng; "Automatic Detection and Segmentation of Ovarian Cancer Using A Multitask Model in Pelvic CT Images", Oxidative Medicine And Cellular Longevity, 2022.
[19] Rahul Gomes; Connor Kamrowski; Pavithra Devy Mohan; Cameron Senor; Jordan Langlois; Joseph Wildenberg; "Application of Deep Learning to IVC Filter Detection from CT Scans", Diagnostics (Basel, Switzerland), 2022.
[20] Sophie Ostmeier; Brian Axelrod; Benjamin F. J. Verhaaren; Abdelkader Mahammedi; Li-Jia Li; Greg Zaharchuk; Soren Christensen; Jeremy J. Heit; "Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists", Arxiv-Eess.Iv, 2022.
[21] Zihui Ouyang; Peng Zhang; Weifan Pan; Qiang Li; "Deep Learning-based Body Part Recognition Algorithm for Three-dimensional Medical Images", Medical Physics, 2022.
[22] Chun-Chieh Wang; Pei-Huan Wu; Gigin Lin; Yen-Ling Huang; Yu-Chun Lin; Yi-Peng Eve Chang; Jun-Cheng Weng; "Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning", Journal Of Personalized Medicine, 2022.
[23] Marin Benčević; Marija Habijan; Irena Galić; "Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network", Arxiv-Eess.Iv, 2022.
[24] Amin Gasmi; "Deep Learning and Health Informatics for Smart Monitoring and Diagnosis", Arxiv-Q-Bio.Qm, 2022.
[25] Li Sun; Junxiang Chen; Yanwu Xu; Mingming Gong; Ke Yu; Kayhan Batmanghelich; "Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis", Ieee Journal Of Biomedical And Health Informatics, 2022.
[26] Manika Jha; Richa Gupta; Rajiv Saxena; "A Framework for In-vivo Human Brain Tumor Detection Using Image Augmentation and Hybrid Features", Health Information Science And Systems, 2022.
[27] Eugene Vorontsov; Pavlo Molchanov; Matej Gazda; Christopher Beckham; Jan Kautz; Samuel Kadoury; "Towards Annotation-efficient Segmentation Via Image-to-image Translation", Medical Image Analysis, 2022.
[28] Lisa C. Adams; Felix Busch; Daniel Truhn; Marcus R. Makowski; Hugo JWL. Aerts; Keno K. Bressem; "What Does DALL-E 2 Know About Radiology?", ARXIV-CS.CV, 2022.
[29] Sarah Ettinger; Lena Sonnow; Christian Plaass; Alexandra Rahn; Christina Stukenborg-Colsman; Christian von Falck; Gesa Poehler; Christoph Becher; "Arthroscopic Defect Size Measurement in Osteochondral Lesions of The Talus Underestimates The Exact Defect Size and Size Measurement with Arthro-MRI (MR-A) and High-resolution Flat-panel CT-arthro Imaging (FPCT-A)", Knee Surgery, Sports Traumatology, Arthroscopy : Official ..., 2022.
[30] Connie Y Chang; Florian A Huber; Kaitlyn J Yeh; Colleen Buckless; Martin Torriani; "Original Research: Utilization of A Convolutional Neural Network for Automated Detection of Lytic Spinal Lesions on Body CTs", Skeletal Radiology, 2023.