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3 articles for “tumor segmentation”
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Image Processing and Deep CNN-based Automatic Liver Cancer Detection
Abstract: Liver cancer ranks among the leading causes of mortality for people worldwide. In the current situation, manually identifying the cancer tissue is a challenging and timeconsuming task. Treatment planning, response monitoring, tumor load assessment, and prediction are all made possible by the segmentation of liver lesions in CT scans. To address the current problem of liver cancer, the Hybridized Fully Convolutional Neural Network (HFCNN), which has been theoretically modeled, has …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 1, 2025 · pp. 39–41 Read article
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Study on Brain Tumor Detection Using Morphological Operations in MATLAB with Graphical User Interface (GUI)
Abstract: Brain tumor detection plays a crucial role in early diagnosis and effective treatment planning. This research presents a MATLAB-based Graphical User Interface (GUI) for Brain Tumor Detection, incorporating a comprehensive pipeline of image processing techniques. The GUI provides a user-friendly platform, empowering medical professionals to accurately and efficiently analyze MRI brain scans. The GUI begins with text removal to eliminate any textual artifacts that may be present in the MRI …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 24–31 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article