An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning

2020 ◽  
Author(s):  
Aditya Khamparia ◽  
Prakash Kumar Singh ◽  
Poonam Rani ◽  
Debabrata Samanta ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Learning Framework

scholarly journals Acral melanoma detection using dermoscopic images and convolutional neural networks

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qaiser Abbas ◽  
Farheen Ramzan ◽  
Muhammad Usman Ghani
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Clinical Decision Making ◽  
Data Augmentation ◽  
Automated System ◽  
Convolutional Network ◽  
Acral Melanoma ◽  
Melanoma Subtypes

AbstractAcral 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 custom-built 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.

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

scholarly journals Big transfer learning for automated skin cancer classification

2021 ◽  
Vol 23 (3) ◽  
pp. 1611
Author(s):  
Zinah Mohsin Arkah ◽  
Dalya S. Al-Dulaimi ◽  
Ahlam R. Khekan
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Image Features ◽  
Cancer Classification ◽  
Automated Classification ◽  
Novel Approach ◽  
Learned Features ◽  
Manual Classification

<p>Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.</p>

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