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Automatic Identification of Extremely Tiny Brain Hemorrhages in Susceptibility Weighted Images using Convolutional Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 43 |
| Year of Publication: 2023 |
| Authors: Sahereh Obeidavi |
10.5120/ijca2023923244
|
Sahereh Obeidavi . Automatic Identification of Extremely Tiny Brain Hemorrhages in Susceptibility Weighted Images using Convolutional Neural Network. International Journal of Computer Applications. 185, 43 ( Nov 2023), 43-48. DOI=10.5120/ijca2023923244
Abstract
Ischemic stroke is an acute cerebrovascular disease that causes long-term disability and even death. Acute lesions that occur in most stroke patients can be eliminated with careful diagnosis and treatment. The presence of acute lesions in a majority of stroke cases necessitates precise diagnosis and treatment for elimination. Despite the sensitivity of MRI imaging to these lesions, accurately gauging their location and volume manually poses challenges for physicians. The manual examination of numerous MRI-generated cross-sections is time-consuming and susceptible to human error. Consequently, the consensus among medical practitioners is that automated segmentation procedures for ischemic stroke lesions can significantly expedite the commencement of treatment. Various methods have been developed to attain this objective, with deep neural networks emerging as notably effective, producing outcomes that are both superior and more precise. Within the realm of deep learning algorithms, the U-Net algorithm has gained popularity in recent years for its accurate response, high precision, rapid processing and learning capabilities, and its independence from large datasets for learning. The U-Net algorithm has become a favored choice for identifying and segmenting image components in the processing of medical images. The proposed segmentation framework comprises two distinct networks: the U-Net convolutional neural network serves as the primary structure of the model, while the Inception convolutional neural network is integrated into each layer of the U-Net network. Incorporating the Inception network within the U-Net network has notably enhanced segmentation accuracy. This report focuses on elucidating the algorithm's intricacies, encompassing its architectures, pre-processing techniques, data pre-preparation, and post-processing methods. The structural aspects of the algorithm, particularly its convolutional network, are explored in depth. Additionally, the optimal configuration for the algorithm's parameters and super parameters is investigated to enhance and achieve peak accuracy in the segmentation of stroke-related images.
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