Melanoma is an early stage of skin cancer. The objective of the proposed work is to detect the symptoms of melanoma early through images of the moles obtained from image processing device and classify the types. The procedure involves converting raw melanoma skin image initially into hue, saturation, and intensity for digital processing. The required information for detecting melanoma is available in the intensity part of the color image. The intensity of the image is down sampled to decrease the bit depth. If the illumination of the down sampled image is not uniform, then gamma correction is applied to get the uniform illumination. A K‐means clustering is applied on gamma corrected image which segments the melanoma part from the skin. Textural features are extracted from the segmented image using gray level co‐occurrence matrix. Machine learning technique is applied to classify the melanoma images into types like lentigo, acral, nodular, and superficial. Melanoma is detected in this process with an accuracy of 90%.
Facial paralysis is a disease that occurs due to the disorder of neuromuscular system. It may affect on one or both sides of the face. Facial paralysis will lead to significant physical and functional hurt to patients. To diagnose the disease, degree of facial paralysis has to be evaluated. The proposed method is to evaluate the degree of facial paralysis by using IECM algorithm. The initial stages of diseases are detected by analyzing the various facial expressions. The proposed method includes preprocessing of images and estimation of level of diseases. The proposed algorithm measures the distance between the eye brows to infra orbital. It also measures the distance between the edges of mouth and lateral canthus. Diseases levels are identified as Normal, mild and severe by using the estimated parameters.
General TermsFacial paralysis, left and right face, IECM Algorithm.
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