The paper investigates computer diagnostic systems, their architectures, methods, and algorithms used in their work to diagnose cancer, including breast, lung, brain, and other tumors.
Traditional and neural network methods for image segmentation and classification are analyzed and compared, and diagnostic tools in medicine are analyzed.
The key approaches to medical image processing are investigated, in particular, the analysis of segmentation methods based on U-Net networks and classification using convolutional neural networks.
It is established that neural network methods outperform traditional approaches in terms of accuracy and efficiency in segmentation and classification tasks. The main advantages of using neural network architectures in computer diagnostic systems are revealed, in particular, the possibility of automating the diagnostic process and improving the accuracy of the results. Neural network-based solutions provide a more adaptive and scalable approach that can be trained and improved as new data becomes available, making them highly suitable for rapidly evolving industries such as medical diagnostics.
Computer experiments on image preprocessing and segmentation were conducted. Thus, the effectiveness of U-Net networks for image segmentation tasks was established.
An automatic diagnostic method based on U-Net and convolutional neural networks has been developed that reduces the diagnostic time due to parallel image processing. The paper presents a detailed scheme of the developed method. It includes the segmentation of immunohistochemical images, after which the quantitative characteristics and classification of histological images will be calculated, followed by the combination of all the results obtained to make a diagnosis. This approach provides a comprehensive analysis that combines structural and quantitative data, which helps to increase the reliability of diagnostic results
The scientific novelty of the developed method of automatic diagnosis based on neural networks is the use of a parallel approach to performing segmentation and classification.
The developed method can be used in computer diagnostic systems in medicine. The use of the developed method gives an increase in the speed of data processing and, accordingly, diagnosis. In addition, parallel execution contributes to more efficient use of computing resources.
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