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Articles by Malik Braik
Total Records ( 2 ) for Malik Braik
  Dheeb Al Bashish , Malik Braik and Sulieman Bani-Ahmad
  The aim of this study is to design, implement and evaluate an image-processing-based software solution for automatic detection and classification of plant leaf diseases. Studies show that relying on pure naked-eye observation of experts to detect and classify such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The methodology of the proposed solution is image-processing-based and is composed of four main phases; in the first phase we create a color transformation structure for the RGB leaf image and then, we apply device-independent color space transformation for the color transformation structure. Next, in the second phase, the images at hand are segmented using the K-means clustering technique. In the third phase, we calculate the texture features for the segmented infected objects. Finally, in the fourth phase the extracted features are passed through a pre-trained neural network. As a testing step we use a set of leaf images taken from Al-Ghor area in Jordan. Present experimental results indicate that the proposed approach can significantly support an accurate and automatic detection and recognition of leaf diseases. The developed Neural Network classifier that is based on statistical classification perform well in all sampled types of leaf diseases and can successfully detect and classify the examined diseases with a precision of around 93%. In conclusion, the proposed detection models based neural networks are very effective in recognizing leaf diseases, whilst K-means clustering technique provides efficient results in segmentation RGB images.
  Basim Alhadidi , Malik Braik and Mohammad I. Salem
  In this study, a thresholding technique was proposed as a mechanism to recognize the malignant cells in epithelial tissues. This is particularly useful for cancer cells diagnosis. Malignant transformation is a genetic operation that causes changes in cells morphology and behavior. Therefore, the evaluation of changes in the cell biology was studied in a way to find out the basis for diagnosing such diseases. The proposed thresholding technique depends on the automation of counting black to white cells ratio and the number of cells in the epithelial tissues samples. This technique has many features, such as: it is able to test hundreds of samples in a less computing time and can provide reliable results and it can give the user a better control over the counting process and can process any part of the sample. The results obtained in this study confirmed the potential of the proposed technique, such that it can judge the malignant cells in epithelial tissues whether normal, abnormal or suspected only in few seconds. Using threshold technique in diagnosing epithelial tissues samples can provide fast and easy way to perform malignant diagnosis (normal, abnormal or suspected) to be used as reference for early diagnosis medical research.
 
 
 
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