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Diabetes is a general illness regularly contaminated in people. Numerous approaches to recognize diabetes, one of them is checking circulatory strain, however, along these lines isn't compelling, in light of the fact that it takes blood first and takes a ton of time. Iridology is a one-way investigation wellbeing dependent on the iris. In this manner, we need an apparatus used to recognize pancreatic harm as a sign of diabetes through iridology. TheBurden picture is the initial step to distinguish pancreatic organs dependent on the iris. The eye picture that we utilized as the info framework originates from the eye facility database. The subsequent stage is a versatile middle sifting utilized in the process preprocessing to lessen the commotion on the picture. After that, the subsequent stage is a division process utilizing Hough circle change technique. The consequences of division will be standardized and take the Region of intrigue. Return for money invested will be done element extraction by utilizing GLCM (Gray Level Co-Occurrence Matrix). To know the state of a pancreas organ utilizing back propagation strategy.

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In spite of the gargantuan number of patients affected by melanoma every year, its detection at an early stage is still a challenging task. This paper illustrates a method which involves the combination of the existing ABCD (Involving symmetry, border, color, and diameter detection) rule and grey level co-occurrence matrix (GLCM) along with Local Binary Pattern (LBP) for identification of malignant melanoma skin lesion with greater accuracy. Several steps, such as image acquisition technique, pre-processing (RGB to HSV) techniques and segmentation processes are undertaken for the skin feature selection criteria to successfully determine the skin lesion's characteristic properties for classification. Texture features such as contrast, entropy, energy and homogeneity of the affected region is obtained using LBP and GLCM for discriminatory purposes of the two cases (melanoma and non-melanoma). Finally, the back propagation neural network (BPN) is used as the classifier to determine whether the dermoscopic image is benign or malignant.

Lung cancer plays a major role among the people who are affected with cancer.  The major reason is the presence of nodule in a lung region. Early diagnosis of this nodule may decrease the severity also increase the life span of a patient. In this paper, a methodology is proposed to detect the lung nodule and nodule region using texture features. Various image processing techniques are used in this paper. CT images are taken as input over MRI because of its advantages over less exposure of radiation[4]. The given input image is denoised by using adaptive bilateral filter and image contrast is improved by the histogram equalization technique. Superpixel segmentation is used for the segmentation process. A Simulation process has been done using MATLAB software.