SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

Naeem, Ahmad; Anees, Tayyaba; Fiza, Makhmoor; Lee, Seung Won

doi:10.3390/s22155652

Cited by 41 publications

(30 citation statements)

References 59 publications

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“…The optimization of classification and feature selection can be effectuated by the revised version of the Red Fox Optimization (DRFO) method that presents outcomes with greater reliability and accuracy. In [16], designed a new structure based on DL for multi-class skin tumor types like Basal Cell Carcinoma, Benign Keratosis, Melanocytic Nevi, and Melanoma. The presented approach is termed SCDNet combined Vgg16 with CNN for classifying various kinds of skin tumours.…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

Srilakshmi¹,

Lydia²,

Ramakrishnaiah

2023

Preprint

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Skin cancer (SC) is the most prevalent cancer globally. Clinical assessment of skin lesions is needed to assess the disease characteristics; but, it is varied in interpretation and limited by longer timelines. Dermoscopy refers to a non-invasive imaging method that permits dermatologists to examine skin lesions with improved visualization of surface and subsurface structures. Machine-learning (ML) and deep learning (DL) methods were established to support dermatologists and overcome the problem as accurate and earlier diagnoses of SC is critical to increasing survival rate of the patient. In recent years, SC classification on dermoscopy images using DL has received great deal of interest. DL algorithms, especially convolutional neural networks (CNNs), have proved promising outcomes in precisely classifying skin lesions and differentiating between benign and malignant cases. The study presents a new Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification (DBOA-MMDLSCC) technique on dermoscopic images. The presented DBOA-MMDLSCC technique exploits the ensemble learning mechanism by the use of three DL models with metaheuristic based hyperparameter tuning. In the presented DBOA-MMDLSCC technique, U-Net++ model is utilized for skin lesion segmentation. For feature extraction, multi-modal DL model comprising three DL models namely Xception, Residual Network (ResNet), and SqueezeNet. Moreover, the hyperparameter tuning of the DL approaches takes place using the DBOA. Lastly, convolutional autoencoder (CAE) model is applied for detecting and classifying SC. A wide range of experiments were performed to exhibit the simulation results of the DBOA-MMDLSCC technique. The experimental values highlighted the improved performance of the DBOA-MMDLSCC technique in terms of different evaluation measures.

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Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

Srilakshmi¹,

Lydia²,

Ramakrishnaiah

2023

Preprint

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“…The obtained image from datasets is imbalanced as discussed in Table 2 . The imbalanced class of the images affected the performance of the model at the time of training [ 77 , 78 , 79 , 80 , 81 , 82 ]. To overcome these issues, we used the SMOTE Tomek technique to increase the numbers of images in the minority class of the datasets [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

Tahir

Naeem

Malik

et al. 2023

Cancers

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Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

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“…Numerous studies, such as [ 50 , 71 , 72 , 73 , 74 , 75 ], have been carried out to identify COVID-19; however, these methodologies do not make use of data sharing to construct an improved prediction model [ 77 , 78 ]. However, several of the algorithms used GAN in conjunction with data augmentation to generate fake CXR images.…”

Section: Resultsmentioning

confidence: 99%

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

Malik

Naeem

Loh

2023

Sensors

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Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients’ right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model’s accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

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SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

Cited by 41 publications

References 59 publications

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

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