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The segmentation of dermoscopic‐based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11‐layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning. [ABSTRACT FROM AUTHOR]

In this paper, authors propose an efficient system for the segmentation of lesions in digital mammograms. The proposed detection system comprises three steps. In the first step, efficient pre-processing technique is developed using top-hat morphological transform, wavelet transform, and morphological opening-closing reconstruction filter followed by median filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM) clustering and Otsu global thresholding method. Finally, computed threshold is used to binarize the pre-processed image thereby yielding the suspicious mass regions from it. The free response operating characteristics (FROC) curve is used to evaluate the performance of the proposed method. Proposed system achieved the sensitivity of 94% at the rate of 1.30 false positives per image. [ABSTRACT FROM AUTHOR]

Infrared thermography is a non destructive temperature measuring technique, widely used in agriculture, process industries, manufacturing, pharmaceutical industries, and construction industries to detect anomalies, to predict possible faults, or to check the quality of the object. In this paper, two systems, real time and off line, are proposed to monitor the temperature variations and analyze hot regions in the electrical assets using infrared thermograms. This novel technique helps to prevent the electrical assets before any catastrophe would happen in the future. The color based segmentation technique is used to blotch hot regions in the thermograms of electrical systems. A redness area based algorithm is also proposed to analyze the hot regions and also to estimate rate of change of hotness in electrical assets for early detection and prediction of faults. [ABSTRACT FROM AUTHOR]