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A research study conducted at the KLS Gogte Institute of Technology in Belagavi, India, has explored the use of super-resolution (SR) reconstruction techniques for improving the spatial resolution of pathology images in prostate cancer diagnosis. The study compared various machine learning, deep learning, and generative adversarial network (GAN) algorithms and evaluated their performance using different metrics. The results showed that the SRGAN algorithm outperformed other algorithms, achieving high-quality image resolution and fine details. The application of SRGAN could provide a cost-effective solution for enhancing the accuracy of cancer diagnosis by improving the visualization of cellular and tissue structures in pathology images. [Extracted from the article]

Researchers from Hebei University in Baoding, China have developed a new algorithm for diagnosing neuroblastoma, a type of malignant solid tumor in children. The algorithm uses a network model based on the U-Net structure, with DenseNet as the encoder, to extract features from whole slide images of neuroblastoma. The algorithm achieved high accuracy in segmenting and counting poorly differentiated and differentiated cells, which can help determine the histopathological types and prognostic effects of neuroblastoma. The research has been peer-reviewed and published in the journal Signal Image and Video Processing. [Extracted from the article]

Researchers from Tehran University of Medical Sciences have developed a new approach for automating the segmentation of glioblastoma multiforme (GBM) tumors from MRI images. The technique utilizes the Marker-controlled watershed segmentation (MCWS) algorithm, along with other image processing techniques, to improve segmentation accuracy. The study highlights the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumors. The MCWS technique is considered an effective and efficient approach for segmenting challenging medical images. [Extracted from the article]

A study conducted by researchers at VIT-AP University in Andhra Pradesh, India, has developed a deep learning algorithm called RU-Net2+ for accurate brain tumor segmentation and survival rate prediction. The algorithm was applied to a dataset of brain tumor images and achieved impressive results, surpassing current benchmarks in classification accuracy, tumor segmentation precision, and survival rate prediction. The framework shows promise for automating brain tumor diagnosis and enhancing patient care, providing valuable insights for medical professionals making treatment decisions. The study was published in IEEE Access. [Extracted from the article]

Keywords: Gwangju; South Korea; Asia; Algorithms; Brain Cancer; Cancer; Diagnostics and Screening; Health and Medicine; Oncology EN Gwangju South Korea Asia Algorithms Brain Cancer Cancer Diagnostics and Screening Health and Medicine Oncology 729 729 1 05/02/23 20230502 NES 230502 2023 MAY 1 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Investigators publish new report on Oncology - Brain Cancer. Gwangju, South Korea, Asia, Algorithms, Brain Cancer, Cancer, Diagnostics and Screening, Health and Medicine, Oncology. [Extracted from the article]

Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models." Keywords: Algorithms; Biomarkers; Cancer; Cyborgs; Diagnostics and Screening; Emerging Technologies; Glioblastomas; Health and Medicine; Machine Learning; Oncology EN Algorithms Biomarkers Cancer Cyborgs Diagnostics and Screening Emerging Technologies Glioblastomas Health and Medicine Machine Learning Oncology 2023 FEB 20 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Data detailed on glioblastomas have been presented. [Extracted from the article]