Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesi...Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality.To this end,we have designed,implemented and analyzed a hybrid approach entailing convolutional neural networks(CNN)and local binary patterns(LBP).The experiments have been performed on publicly accessible datasets ISIC 2017,2018 and 2019(HAM10000)with data augmentation for in-distribution generalization.As a novel contribution,the CNN architecture is enhanced with an intelligible layer,LBP,that extracts the pertinent visual patterns.Classification of Basal Cell Carcinoma,Actinic Keratosis,Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing,respectively.Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%,sensitivity of 95.63%and specificity of 97.90%.Hence,the proposed approach can be used in research and clinical settings to provide second opinions,closely approximating experts’intuition.展开更多
In this Letter, we have proposed a generalized Gaussian probability density function（GGPDF）-based method to estimate the symbol error ratio（SER） for pulse amplitude modulation（PAM-4） in an intensity modulation/d...In this Letter, we have proposed a generalized Gaussian probability density function（GGPDF）-based method to estimate the symbol error ratio（SER） for pulse amplitude modulation（PAM-4） in an intensity modulation/direct detection（IM/DD） system. Furthermore, a closed form expression of SERGGDfor PAM-4 has been derived. The performance of the proposed method is evaluated through simulation as well as experimental work.The fitting of probability density functions of the received signal is applied via GGPDF and shape parameters P1 and P2 associated with different PAM-4 levels are determined. The optimum single value of shape parameter P is then calculated to estimate the SER. The mathematical relationship of P with different received optical powers and receiver bandwidths has been determined and verified. The proposed method is a fast and accurate method to estimate SER of a PAM-4 system, which is more reliable and in agreement with the error counting method.展开更多
文摘Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality.To this end,we have designed,implemented and analyzed a hybrid approach entailing convolutional neural networks(CNN)and local binary patterns(LBP).The experiments have been performed on publicly accessible datasets ISIC 2017,2018 and 2019(HAM10000)with data augmentation for in-distribution generalization.As a novel contribution,the CNN architecture is enhanced with an intelligible layer,LBP,that extracts the pertinent visual patterns.Classification of Basal Cell Carcinoma,Actinic Keratosis,Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing,respectively.Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%,sensitivity of 95.63%and specificity of 97.90%.Hence,the proposed approach can be used in research and clinical settings to provide second opinions,closely approximating experts’intuition.
文摘In this Letter, we have proposed a generalized Gaussian probability density function（GGPDF）-based method to estimate the symbol error ratio（SER） for pulse amplitude modulation（PAM-4） in an intensity modulation/direct detection（IM/DD） system. Furthermore, a closed form expression of SERGGDfor PAM-4 has been derived. The performance of the proposed method is evaluated through simulation as well as experimental work.The fitting of probability density functions of the received signal is applied via GGPDF and shape parameters P1 and P2 associated with different PAM-4 levels are determined. The optimum single value of shape parameter P is then calculated to estimate the SER. The mathematical relationship of P with different received optical powers and receiver bandwidths has been determined and verified. The proposed method is a fast and accurate method to estimate SER of a PAM-4 system, which is more reliable and in agreement with the error counting method.
