![]() The proposed approach can successfully detect and classify the examined diseases with a precision between 83% and 94%, and able to achieve 20% speedup over the approach proposed in. The experimental results indicate that the proposed technique is a fast and accurate technique for the detection of plant leaves diseases. ![]() values and the pixels on the boundaries of the infected cluster are completely removed. Depending on the severity, this disease can cause tree death. It affects a large number of shade trees including elm, catalpa, hackberry, ginkgo, oak, sycamore, maple, mulberry, and sweetgum in the landscape. The other additional step is that the pixels with zeroes R.G.B. Bacterial leaf scorch (Xylella fastidiosa) is a disease of shade trees in Maryland. And in second step, these green pixels are masked based on their specific threshold values which will computed using Otsu's method, then those mostly green pixels are masked. In the first step we find the mostly green colored pixels. The following extra two steps are required to add successively after the segmentation phase. The process consists of four main phases as mentioned in. Which is an improvement to the solution proposed in as it will be able to provide quick and more accurate solution. We proposed software solution for automatic classification and detection of plant leaf diseases. ![]() Main advantage of this method is that it will give fast and accurate result with the help of training data set and it reduces time and computation power. In this paper Probabilistic Neural Network is used for decision making which is followed by Image Preprocessing with Gaussian Filter Method, Image segmentation and detection by using Threshold Based Segmentation Method, Feature extraction by Gray Level Co-occurrence Matrix (GLCM)features, and Dimensionality reduction by Principal Component Analysis. Artificial intelligence and image processing have enormous growth in medical research field with the help of neural network and fuzzy logic. Image may contain some noise due to error in machine performance which will result in inaccuracy and becomes hazardous to patient suffering from this disease. But the time require for this is more and result may not be accurate. Previously this decision is taken manually by humans with the help of MR (Magnetic resonance) or CT (Computerized Tomography) scan image of brain. This proposed system includes several steps segmentation for tumor detection, feature extraction, dimensionality reduction of extracted feature for removing redundant features and classification of tumor. This paper describe the proposed system for brain tumor detection and classification along with the help of Artificial neural network. Detection and classification of tumor from MRI brain image is becoming most challenging area to research.
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