Interestingly the cases with positive lymph node metastasis demonstrated Grade 3 CC.Hence, during routine histopathological examination, the search of CC can be considered as one of the important parameters to note the aggressive nature of OSCC.
Work on carpet appearance change is reported, focusing on new textural measure ments of carpet pile. Three instrumental techniques to assess this texture change have been developed: image analysis, goniophotometry, and densitometry. A set of specially prepared saxony-type polyester carpet constructions were tested, both as unworn con trols and service worn to 60,000 steps of foot traffic. In Part I, actual carpet samples and their photographs were analyzed using computerized image analysis. This tech nique may prove valuable in assessing meaningful differences in carpet appearance, and particularly textural changes caused by pile/tuft definition or coherence. Gray level histograms were correlated with changes in appearance, differenoes in tuft spacing, and related carpet properties.
Further work on carpet appearance change is reported, focusing on new textural measurements of carpet pile. These techniques have included image analysis, goniophotometry, and densitometry. A set of specially prepared saxony-type polyester carpet constructions were tested, both as unworn controls and service-worn to 60,000 steps of foot traffic. In Part II of this series, the results of goniophotometry, i.e. variable angle reflectometry, are reported. Techniques are presented to generate goniophotometric reflectance curves, which can be analyzed for directional pile lay in carpet. These techniques appear promising in characterizing certain changes in pile shading and wear patterns, both in unworn and service-worn carpet.
Automated melanoma recognition using image processing technique from the available dermoscopic images in deep learning is difficult task because of the contrast and variation of melanoma in skin. It is mainly a non-invasive method so that it cannot contact with skin more forcefully. To overcome these disadvantages this research work proposes a method using very deep convolutional neural networks (CNNs). For more accurate classification in this method we are using FCRN and CNN with the effective training limited data. Initially, Performance of Segmentation is done using residual networks using a image from the dataset followed by Classification by neural networks to check the abnormalities in skin. In this kind of classification technique the network has more specified features from the segmented portion alone. The proposed technique is mainly evaluated on datasets and experimental results that would show the performance in histogram and PSNR ratio.
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