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%.

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Iris biometrics is one of the fastest-growing technologies, and it has received a lot of attention from the community. Iris-biometric-based human recognition does not require contact with the human body. Iris is a combination of crypts, wolflin nodules, concentrated furrows, and pigment spots. The existing methods feed the eye image into deep learning network which result in improper iris features and certainly reduce the accuracy. This research study proposes a model to feed preprocessed accurate iris boundary into Alexnet deep learning neural network-based system for classification. The pupil centre and boundary are initially recorded and identified from the given eye images. The iris boundary and the centre are then compared for the identification using the reference pupil centre and boundary. The iris portion, exclusive feature of the pupil area is segmented using the parameters of multiple left-right point (MLRP) algorithms. The Alexnet deep learning multilayer networks 23, 24, and 25 are replaced according to the segmented iris classes. The remaining Alexnet layers are trained using the square gradient decay factor (GDF) in accordance with the iris features. The trained Alexnet iris is validated using suitable classes. The proposed system classifies the iris with an accuracy of 99.1%. The sensitivity, specificity, and F1-score of the proposed system are 99.68%, 98.36%, and 0.995. The experimental results show that the proposed model has advantages over current models.

Iris recognition plays an important role in the Biometric authentication. The eye lids, lashes and flash light impressions are hazard, which in turn reduces successive iris recognition rate. The proposed method includes the preprocessing of images such as image filter, morphological operations, and edge detection, which finds the exact pupil part. The proposed method uses the MLRP algorithm, to identify the exact iris layers rather than the existing methods. The key feature is extracted in iris layer. The method is applied on both left and right iris, which gives unique key between left and right eye for every person. The extracted key feature identifies the eye even in the different eye position, which gives the repeatability. The proposed method is tested with the CASIA data base iris images, which consists of left and right eye set for the different human. The proposed method reduces the FAR to 15.6% and FRR to 14%. General TermsBiometric -Image processing, Image repeatability.

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