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

Although automated Skin Lesion Classification (SLC) is a crucial integral step in computer-aided diagnosis, it remains challenging due to variability in textures, colors, indistinguishable boundaries, and shapes. This article proposes an automated dermoscopic SLC framework named Dermoscopic Expert (...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Informatics in medicine unlocked Ročník 28; s. 100819
Hlavní autori:	Hasan, Md. Kamrul, Elahi, Md. Toufick E., Alam, Md. Ashraful, Jawad, Md. Tasnim, Martí, Robert
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 2022 Elsevier
Predmet:	Convolutional neural networks Image augmentation ISIC skin lesion datasets Skin lesion classification and segmentation Transfer learning ISIC skin lesion datasets Image augmentation Skin lesion classification and segmentation Convolutional neural networks Transfer learning
ISSN:	2352-9148, 2352-9148
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Although automated Skin Lesion Classification (SLC) is a crucial integral step in computer-aided diagnosis, it remains challenging due to variability in textures, colors, indistinguishable boundaries, and shapes. This article proposes an automated dermoscopic SLC framework named Dermoscopic Expert (DermoExpert). It combines the pre-processing and hybrid Convolutional Neural Network (hybrid-CNN). The proposed hybrid-CNN has three distinct feature extractor modules, which are fused to achieve better-depth feature maps of the lesion. Those single and fused feature maps are classified using different fully connected layers, then ensembled to predict a lesion class. In the proposed pre-processing, we apply lesion segmentation, augmentation (geometry- and intensity-based), and class rebalancing (penalizing the majority class’s loss and merging additional images to the minority classes). Moreover, we leverage transfer learning from the pre-trained models. Finally, we deploy the weights of our DermoExpert to a possible web application. We 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 improve the 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 3.0% for the ISIC-2018 dataset concerning a balanced accuracy. Since DermoExpert provides better classification outcomes on three different datasets, leading to a better recognition tool to assist dermatologists. Our source code and segmented masks for the ISIC-2018 dataset will be available as a public benchmark for future improvements. [Display omitted] •Proposing a hybrid-CNN classifier for multiple skin diseases recognition.•Precisely segmenting skin lesion although the presence of hair fibers and other artifacts.•Class-rebalancing, transfer learning, and augmentation for a generic model, as tiny datasets are being used.•State of the art results on ISIC-16 (2-class), ISIC-17 (3-class), and ISIC-18 (7-class).•Development of a possible web application, deploying our trained model’s weights.
ISSN:	2352-9148 2352-9148
DOI:	10.1016/j.imu.2021.100819