A Deep CNN Model for Skin Cancer Detection and Classification

Junayed, Masum Shah; Anjum, Nipa; Sakib, Abu Noman; Islam, Md Baharul

doi:10.24132/csrn.2021.3002.8

Cited by 8 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2021, Masum Shah Junayed et al [2] used a deep convolutional neural network to develop a deep learning model for detecting and classifying skin cancer. They applied augmentation techniques to increase the Dhaka Medical College (DMC) data-set, which includes data on four different forms of skin cancer.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Deep Learning Framework for Skin Cancer Classification using Dermoscopy Images and Metadata

Mohamed,

Abubakr,

Abdu

et al. 2023

Preprint

View full text Add to dashboard Cite

Skin exposed to the sun is more likely to have skin cancer, which is an unnatural growth of skin cells. However, skin that isn’t frequently exposed to sunlight might potentially develop this prevalent type of cancer. This study attempts to detect and categorize six different types of skin cancer using clinical images accurately. The proposed approach used both clinical images and Metadata to feed into a hybrid model that benefits from deep learning in feature extraction and machine learning in classification. Many experiments have been applied to the PAD-UFES-20 dataset. Seven distinct algorithms of machine learning are applied with ten different pre-trained deep learning models. The hybrid model consists of MobileNetV2 as a feature extractor and logistic regression as a classifier for images, and random forest as a classi-fier for metadata has proved its superiority. The accuracy was 95.6%, precision 96.8%, recall 95.6%, F1-Score 95.7%. The evaluation process shows that metadata has an effective role in improving accuracy compared to previous studies that depend on images only in classification.

show abstract

Section: Related Workmentioning

confidence: 99%

A Hybrid Deep Learning Framework for Skin Cancer Classification using Dermoscopy Images and Metadata

Mohamed,

Abubakr,

Abdu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Machine Vision-Based Expert System for Automated Skin Cancer Detection

Junayed

Jeny

Rada

et al. 2022

Intelligent Computing Systems

View full text Add to dashboard Cite

Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm

Balaha

Hassan

2022

Neural Comput & Applic

View full text Add to dashboard Cite

Skin cancer affects the lives of millions of people every year, as it is considered the most popular form of cancer. In the USA alone, approximately three and a half million people are diagnosed with skin cancer annually. The survival rate diminishes steeply as the skin cancer progresses. Despite this, it is an expensive and difficult procedure to discover this cancer type in the early stages. In this study, a threshold-based automatic approach for skin cancer detection, classification, and segmentation utilizing a meta-heuristic optimizer named sparrow search algorithm (SpaSA) is proposed. Five U-Net models (i.e., U-Net, U-Net++, Attention U-Net, V-net, and Swin U-Net) with different configurations are utilized to perform the segmentation process. Besides this, the meta-heuristic SpaSA optimizer is used to perform the optimization of the hyperparameters using eight pre-trained CNN models (i.e., VGG16, VGG19, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, NASNetMobile, and NASNetLarge). The dataset is gathered from five public sources in which two types of datasets are generated (i.e., 2-classes and 10-classes). For the segmentation, concerning the “skin cancer segmentation and classification” dataset, the best reported scores by U-Net++ with DenseNet201 as a backbone architecture are 0.104, $$94.16\%$$ 94.16 % , $$91.39\%$$ 91.39 % , $$99.03\%$$ 99.03 % , $$96.08\%$$ 96.08 % , $$96.41\%$$ 96.41 % , $$77.19\%$$ 77.19 % , $$75.47\%$$ 75.47 % in terms of loss, accuracy, F1-score, AUC, IoU, dice, hinge, and squared hinge, respectively, while for the “PH2” dataset, the best reported scores by the Attention U-Net with DenseNet201 as backbone architecture are 0.137, $$94.75\%$$ 94.75 % , $$92.65\%$$ 92.65 % , $$92.56\%$$ 92.56 % , $$92.74\%$$ 92.74 % , $$96.20\%$$ 96.20 % , $$86.30\%$$ 86.30 % , $$92.65\%$$ 92.65 % , $$69.28\%$$ 69.28 % , and $$68.04\%$$ 68.04 % in terms of loss, accuracy, F1-score, precision, sensitivity, specificity, IoU, dice, hinge, and squared hinge, respectively. For the “ISIC 2019 and 2020 Melanoma” dataset, the best reported overall accuracy from the applied CNN experiments is $$98.27\%$$ 98.27 % by the MobileNet pre-trained model. Similarly, for the “Melanoma Classification (HAM10K)” dataset, the best reported overall accuracy from the applied CNN experiments is $$98.83\%$$ 98.83 % by the MobileNet pre-trained model. For the “skin diseases image” dataset, the best reported overall accuracy from the applied CNN experiments is $$85.87\%$$ 85.87 % by the MobileNetV2 pre-trained model. After computing the results, the suggested approach is compared with 13 related studies.

show abstract

A Deep CNN Model for Skin Cancer Detection and Classification

Cited by 8 publications

References 17 publications

A Hybrid Deep Learning Framework for Skin Cancer Classification using Dermoscopy Images and Metadata

A Hybrid Deep Learning Framework for Skin Cancer Classification using Dermoscopy Images and Metadata

Machine Vision-Based Expert System for Automated Skin Cancer Detection

Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm

Contact Info

Product

Resources

About