Online ISSN: 2515-8260

Keywords : Soft Computing


An Efficient Brain Tumor Classification And Detection Using Evolutionary Approach For Healthcare System

Vinoth Kumar. V; Dr. Paluchamy. B

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 658-670

The growth of irregular cells within the brain region with the prearrangement of tissues is characterized as a Brain Tumor that leads death to people. Comparing to other categories of cancers, a brain tumor is the most deadly disease that has to be detected and treated in the previous stage. Due to cells' complex formation, the tumor detection process is complicated with simple image processing methodologies. Moreover, for providing proper and efficient treatment to the patients, an exact cancer segmentation and classification technique must process the input of brain images as Magnetic Resonance Imaging (MRI) scans. Based on that, this paper develops a novel approach called Soft Computing based Brain Tumor Detection and Classification (SC-BTDC) with the obtained MRI. In the present scenario of tumor detection from image processing, soft computing techniques play a significant role. Hence, it is adopted in this work. The method contains phases such as pre-processing, Fuzzy c-Means clustering-based segmentation, feature extraction, and image classification. The median pre-processing filter and edge detection methods are incorporated for noise removal and clearly define the image in the stage of the median pre-processing filter. Further, FCM based clustering is performed for the image segmentation process, following, factors of Gray Level Co-Occurrence Matrix (GLCM) found feature extraction is established. The final phase includes the classification process using the soft computing technique called Artificial Neural Network (ANN) classification. The proposed system acquires a higher accuracy rate and is compared with various existing algorithms for proving the efficiency and minimum loss of the proposed algorithm.

DETECTION AND CATEGORIZATION OF PLANT LEAF DISEASES USING NEURAL NETWORKS

V. Praveena; P. Chinnasamy; P. Muneeswari; R. Ananthakumar; Bensujitha .

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2438-2445

-Plants are very necessary for the earth and for all living organisms. Plants maintain the atmosphere. Plant illness, an impairment of the traditional state of a plant that interrupts or modifies its very important functions. All species of plants, wild and cultivated alike, are subject to illness. These diseases occur totally on leaves, but some might also occur on stems and fruits. Leaf diseases are the foremost common diseases of most plants. Plant pathology is the science study of pathogens and environmental circumstances causing illnesses in crops. Organisms causing transmissible disease include fungi, oomycetes, bacteria, viruses, viroids, etc. The latest technique involves automated classification of diseases from plant leaf images neural networks persecution approach called hunting enhancement of microorganisms primarily focused on executing Neural system relies on planar basic principle. Throughout this article, classic neural network algorithms are used to detect and classify the areas infected with multiple illnesses on the plant leaves in order to increase the velocity and precision of the network. The region's increasing formula will improve the network's potency by searching and grouping seed points with prevalent feature extraction method characteristics. The scheduled methodology achieves greater precision in disease detection and classification.