In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).
Breast cancer is the most common cancer in women worldwide and it remains the most common cause of cancer-related death in woman globally. Machine Learning techniques have been proven to be of great help in prognosis and diagnosis of various health related issues. This work constitutes a comparison of five machine learning (ML) algorithms: Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Decision Tree (DT), Random Forest (RF) on the Breast Cancer Wisconsin Diagnostic (BCWD) dataset. Features were extracted from the digitized images of FNA tests on a breast mass. Results show that Random Forests performs better among all the models across different classification metrics such as accuracy, precision, recall, and f1-score.
Neuroimaging is critical in the diagnosis and treatment of brain cancers; however, the first detection of tumors is a challenge. Detection techniques like image segmentation are heavily reliant on the segmented image’s resolution. Magnetic resonance imaging (MRI) tumor segmentation has emerged as a new study area in the medical imaging field. This spongy and delicate mass of tissue is the brain. Stable conditions allow for patterns to enter and interact with each other. To put it simply, a tumor is a mass of tissue that has grown unchecked by the natural mechanisms that keep it under control. When cells divide uncontrollably, they create a cancerous tumor. Brain tumors can be detected and segmented using a variety of methods. A new method for detecting brain tumors using MRI images is presented in this research. An innovative Woelfel filter is used for enhancement, and morphological segmentation approaches combined with anisotropic diffusion are used for segmentation. Segmentation of brain tumors can be accomplished using thresholding and morphological techniques, which are both effective. The tumor will be located and identified using morphological image processing. Image denoising refers to the process of removing artefacts such as noise and aliasing from digital images. Here MATLAB programming language is utilised as it incorporates all the toolboxes required for the application involved in the work.
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