Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits th...Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.展开更多
BACKGROUND Porokeratosis(PK)is a common autosomal dominant chronic progressive dyskeratosis with various clinical manifestations.Based on clinical manifestations,porokeratosis can be classified as porokeratosis of mib...BACKGROUND Porokeratosis(PK)is a common autosomal dominant chronic progressive dyskeratosis with various clinical manifestations.Based on clinical manifestations,porokeratosis can be classified as porokeratosis of mibelli,disseminated superficial porokeratosis,disseminated superficial actinic porokeratosis,linear porokeratosis(LP),porokeratosis palmaris et plantaris disseminata,porokeratosis punctata,popular PK,hyperkeratosis PK,inflammatory PK,verrucous PK,and mixed types.We report a case of LP in a child and describe its dermoscopic findings.CASE SUMMARY Linear porokeratosis is a rare PK.The patient presented with unilateral keratinizing maculopapular rash of the foot in childhood.The patient underwent skin pathology and dermoscopy,and was treated with liquid nitrogen freezing and topical drugs.CONCLUSION From this case we take-away that LP is a rare disease,by the dermoscopic we can identify it.展开更多
Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using...Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.展开更多
Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor dif...Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.展开更多
针对现有皮肤镜图像分割技术分割精度不高的问题,提出了一种基于多尺度信息提取和特征融合的U型网络(multi-scale information extraction and feature fusion U-shaped network,MF-UNet)模型。在U-Net的基础上,在卷积层后加入批归一化...针对现有皮肤镜图像分割技术分割精度不高的问题,提出了一种基于多尺度信息提取和特征融合的U型网络(multi-scale information extraction and feature fusion U-shaped network,MF-UNet)模型。在U-Net的基础上,在卷积层后加入批归一化层,将原本的跳跃连接部分替换为4级特征融合模块,充分利用语义信息和位置信息,在特征提取端末尾加入多尺度空洞卷积模块和多尺度池化模块,增大感受野,利用双路拼接上采样模块进行上采样,减少图像恢复过程中的信息损失。实验表明,相较于U-Net模型,MF-UNet在平均交并比(mean intersection over union,MIoU)上提升了14.32%,在戴斯相似系数(Dice similarity coefficient,DSC)上提升了13.18%,取得了较好的结果。该研究为计算机技术辅助医生进行皮肤病诊断提供了借鉴。展开更多
医学图像的自动分割对于计算机辅助诊断具有重要意义。针对皮肤镜图像分割任务中,本文提出一种RA-UNet(Residual and Attentional-UNet)的分割方法。将原本的网络结构加深,在编码端卷积层引入残差机制减少信息丢失同时防止梯度消失或爆...医学图像的自动分割对于计算机辅助诊断具有重要意义。针对皮肤镜图像分割任务中,本文提出一种RA-UNet(Residual and Attentional-UNet)的分割方法。将原本的网络结构加深,在编码端卷积层引入残差机制减少信息丢失同时防止梯度消失或爆炸,每层采用两个残差特征提取模块充分提取学习特征;同时,每层引入改进的卷积注意力模块(Convolutional block attention block,CBAM)使模型更好地学习皮肤病理区域特征。在ISIC 2018数据集上对所提出的RA-UNet模型进行训练和测试,并与UNet和其他模型进行对比试验,实验结果中准确率(ACC)达到了93.82%,特异度(SP)达到了95.26%,灵敏度(SE)达到了90.78%,精准度(P)达到了90.04%,平均交并比(Miou)达到了86.89%,Dice相似指数(DSC)达到了0.900,整体优于其它模型。对于提高基于图像分析的皮肤病诊断具有一定的参考应用价值。展开更多
基金supported by the School of Computing,Faculty of Engineering,Universiti Teknologi Malaysia,Johor Bahru,81310 Skudai,Malaysia.
文摘Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.
文摘BACKGROUND Porokeratosis(PK)is a common autosomal dominant chronic progressive dyskeratosis with various clinical manifestations.Based on clinical manifestations,porokeratosis can be classified as porokeratosis of mibelli,disseminated superficial porokeratosis,disseminated superficial actinic porokeratosis,linear porokeratosis(LP),porokeratosis palmaris et plantaris disseminata,porokeratosis punctata,popular PK,hyperkeratosis PK,inflammatory PK,verrucous PK,and mixed types.We report a case of LP in a child and describe its dermoscopic findings.CASE SUMMARY Linear porokeratosis is a rare PK.The patient presented with unilateral keratinizing maculopapular rash of the foot in childhood.The patient underwent skin pathology and dermoscopy,and was treated with liquid nitrogen freezing and topical drugs.CONCLUSION From this case we take-away that LP is a rare disease,by the dermoscopic we can identify it.
基金the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/80/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.
文摘Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.
文摘针对现有皮肤镜图像分割技术分割精度不高的问题,提出了一种基于多尺度信息提取和特征融合的U型网络(multi-scale information extraction and feature fusion U-shaped network,MF-UNet)模型。在U-Net的基础上,在卷积层后加入批归一化层,将原本的跳跃连接部分替换为4级特征融合模块,充分利用语义信息和位置信息,在特征提取端末尾加入多尺度空洞卷积模块和多尺度池化模块,增大感受野,利用双路拼接上采样模块进行上采样,减少图像恢复过程中的信息损失。实验表明,相较于U-Net模型,MF-UNet在平均交并比(mean intersection over union,MIoU)上提升了14.32%,在戴斯相似系数(Dice similarity coefficient,DSC)上提升了13.18%,取得了较好的结果。该研究为计算机技术辅助医生进行皮肤病诊断提供了借鉴。
