Skin Cancer Disease Detection Using Transfer Learning Technique

Rashid, Javed; Ishfaq, Maryam; Ali, Ghulam; Saeed, Muhammad Rizwan; Hussain, Mubasher; Alkhalifah, Tamim; Alturise, Fahad; Samand, Noor

doi:10.3390/app12115714

Cited by 69 publications

(38 citation statements)

References 61 publications

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“…Using a massive dataset of 33,126 images, we achieved a validation accuracy of 98.39%. Using the pre-trained model ResNet and VGGNet, [23] attained an accuracy of 0.9208 and 0.8870. [24] used MobileNetV2 and obtained an accuracy of 0.8500.…”

Section: Resultsmentioning

confidence: 99%

Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning

Singha

Roy

2022

J. Inf. Syst. Eng. Bus. Intell.

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Background: Skin cancer can quickly become fatal. An examination and biopsy of dermoscopic pictures are required to determine if skin cancer is malignant or benign. However, these examinations can be costly. Objective: In this research, we proposed deep learning (DL)-based approach to identify a melanoma, the most dangerous kind of skin cancer. DL is particularly excellent in learning traits and predicting cancer. However, DL requires a vast number of images. Method: We used image augmentation and transferring learning to categorise images into benign and malignant. We used the public ISIC 2020 database to train and test our models. The ISIC 2020 dataset classifies melanoma as malignant. Along with the categorization, the dataset was examined for variation. The training and validation accuracy of three of the best pre-trained models were compared. To minimise the loss, three optimizers were used: RMSProp, SGD, and ADAM. Results: We attained training accuracy of 98.73%, 99.12%, and 99.76% using ResNet, VGG16, and MobileNetV2, respectively. We achieved a validation accuracy of 98.39% using these three pre-trained models. Conclusion: The validation accuracy of 98.39% outperforms the prior pre-trained model. The findings of this study can be applied in medical science to help physicians diagnose skin cancer early and save lives. Keywords: Deep Learning, ISIC 2020, Pre-trained Model, Skin Cancer, Transfer Learning

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Section: Resultsmentioning

confidence: 99%

Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning

Singha

Roy

2022

J. Inf. Syst. Eng. Bus. Intell.

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“…Figure 6 depicts sample images from the dataset. To enrich this dataset, data augmentation is employed [62,63]. The number of images in the dataset after augmentation is 1310 images.…”

Section: A Datasetmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

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“…The mortality rates are rising to alarming levels, yet if patients are detected and treated promptly, their chances of surviving are better than 95% [ 29 , 30 , 31 , 32 , 33 , 34 ]. Thus, this motivates us to develop a model for the early diagnosis of skin cancer to save human lives.…”

Section: Introductionmentioning

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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Skin Cancer Disease Detection Using Transfer Learning Technique

Cited by 69 publications

References 61 publications

Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning

Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

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

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