The segmentation of brain tumors in magnetic resonance imaging plays a significant role in the field of image processing. This process has high computational complexity when handled manually by clinical experts. The accuracy in classifying and segmenting the brain tumor depends on the radiologists' experience. The computer‐aided diagnosis‐based brain tumor segmentation approach is proposed to overcome the existing limitations. The proposed convolutional neural network and support vector machine approach consists of the following stages. In the preprocessing stage, unwanted noise and intensity inhomogeneity are suppressed using an anisotropic diffusion filter. Then, the features are extracted using the deep convolutional neural network, and based on the features; the input brain image is classified into normal or abnormal using a support vector machine classifier. The proposed method gives a more successful accuracy rate of 2.11%. Compared with the other methods, the sensitivity and specificity values are also improved to 4.79% and 1.19%.
The contribution of a plant is highly important for both human life and environment. Diseases will affect plant, like all humans and animals. Various diseases may affect plant which disturbs the plants normal growth. Leaf, stem, fruit, root, and flower of the plant may get affected by these diseases. Without proper care the plant may die or its leaves, flowers, and fruits drop. Finding of such infections is required for exact distinguishing proof and treatment of plant sicknesses. The current technique for plant malady discovery utilizes human contribution for distinguishing proof and characterization of illnesses and these strategies endure with time-unpredictability. PC supported programmed division of illnesses from plant leaf utilizing delicate registering can be fundamentally valuable than the current techniques. In this paper, we proposed a method using Artificial neural network (ANN) for identification, classification and segmentation of diseases in plant leaf automatically. In the proposed system capturing the leaf images is done first and then contrast of the image is improved by using Contrast Limited Adaptive Histogram Equalization(CLAHE) method. Then, color and texture features are extracted from the segmented outputs and the ANN classifier is then trained by using that features and it could able to separate the healthy and diseased leaf samples properly. Exploratory outcomes demonstrate that the arrangement execution by ANN taking list of capabilities is better with an exactness of 98%.
Brain tumor and stroke are two important causes of death in and around the world. Tumor classification and retrieval system plays a vital role in medical field. Tumor detection, segmentation and MR imaging seizures are a major concern, although it can be a daunting and tedious task for clinical specialists, the accuracy of which depends solely on their experience. In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. The method has four stages. In the first step, raw MR images are pre-processed by the anisotropic filter, and in the second phase, the removal of the skull is classified by type. The third phase involves the functioning of singular value decomposition and principle component analysis. Finally, the NFBCS method is used to detect and classify tumors and the BCS algorithm optimizes the study model for better classification accuracy.
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