Skin Lesion Classification Using Deep Convolutional Neural Network and Transfer Learning Approach

2021 ◽  
pp. 327-335
Author(s):  
Pravin Kumar Samanta ◽  
Nirmal Kumar Rout
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Deep Convolutional Neural Network ◽  
Learning Approach ◽  
Lesion Classification

scholarly journals Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation

10.2196/18438 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e18438
Author(s):  
Arnab Ray ◽  
Aman Gupta ◽  
Amutha Al
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Process Development ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Network Process ◽  
Lesion Classification

Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

scholarly journals Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation (Preprint)

2020 ◽  
Author(s):  
Arnab Ray ◽  
Aman Gupta ◽  
Amutha Al
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Process Development ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Network Process ◽  
Lesion Classification

BACKGROUND Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. OBJECTIVE We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. METHODS We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. RESULTS We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. CONCLUSIONS Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

scholarly journals DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation

2021 ◽  
Author(s):  
Md. Kamrul Hasan ◽  
Md. Toufick E Elahi ◽  
Md. Ashraful Alam ◽  
Md. Tasnim Jawad
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Web Application ◽  
Learning Strategy ◽  
Characteristic Curve ◽  
Feature Maps ◽  
Lesion Recognition ◽  
Lesion Classification

AbstractBackground and ObjectiveAlthough automated Skin Lesion Classification (SLC) is a crucial integral step in computeraided diagnosis, it remains challenging due to inconsistency in textures, colors, indistinguishable boundaries, and shapes.MethodsThis article proposes an automated dermoscopic SLC framework named Dermoscopic Expert (DermoExpert). The DermoExpert consists of preprocessing and hybrid Convolutional Neural Network (hybrid-CNN), leveraging a transfer learning strategy. The proposed hybrid-CNN classifier has three different feature extractor modules taking the same input images, which are fused to achieve better-depth feature maps of the corresponding lesion. Those unique and fused feature maps are classified using different fully connected layers, which are then ensembled to predict the lesion class. We apply lesion segmentation, augmentation, and class rebalancing in the proposed preprocessing. We have also employed geometry- and intensity-based augmentations and class rebalancing by penalizing the majority class’s loss and combining additional images to the minority classes to enhance lesion recognition outcomes. Moreover, we leverage the knowledge from a pre-trained model to build a generic classifier, although small datasets are being used. In the end, we design and implement a web application by deploying the weights of our DermoExpert for automatic lesion recognition.ResultsWe evaluate our DermoExpert on the ISIC-2016, ISIC-2017, and ISIC-2018 datasets, where the DermoExpert has achieved the area under the receiver operating characteristic curve (AUC) of 0.96, 0.95, and 0.97, respectively. The experimental results defeat the recent state-of-the-art by the margins of 10.0 % and 2.0 % respectively for the ISIC-2016 and ISIC-2017 datasets in terms of AUC. The DermoExpert also outperforms by a border of 3.0 % for the ISIC-2018 dataset concerning a balanced accuracy.ConclusionSince our framework can provide better-classification outcomes on three different test datasets, it can lead to better-recognition of melanoma to assist dermatologists. Our source code and segmented masks for the ISIC-2018 dataset will be publicly available for further improvements.

scholarly journals DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation

2022 ◽  
pp. 100819
Author(s):  
Md. Kamrul Hasan ◽  
Md. Toufick E. Elahi ◽  
Md. Ashraful Alam ◽  
Md. Tasnim Jawad ◽  
Robert Martí
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Lesion Classification
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