scholarly journals Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

2021 ◽  
Vol 11 (22) ◽  
pp. 10593
Author(s):  
Nabeela Kausar ◽  
Abdul Hameed ◽  
Mohsin Sattar ◽  
Ramiza Ashraf ◽  
Ali Shariq Imran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Classification ◽  
Majority Voting ◽  
Ensemble Classification ◽  
Classification Models ◽  
Ensemble Models ◽  
Average Accuracy ◽  
Diagnosis Accuracy ◽  
The Individual

Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. Therefore, the classification of skin cancer using machine learning can be beneficial in the diagnosis and treatment of the patients. Several researchers developed skin cancer classification models for binary class but could not extend the research to multiclass classification with better performance ratios. We have developed deep learning-based ensemble classification models for multiclass skin cancer classification. Experimental results proved that the individual deep learners perform better for skin cancer classification, but still the development of ensemble is a meaningful approach since it enhances the classification accuracy. Results show that the accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResNetV2, and VGG-19 are 72%, 91%, 91.4%, 91.7% and 91.8%, respectively. The accuracy of proposed majority voting and weighted majority voting ensemble models are 98% and 98.6%, respectively. The accuracy of proposed ensemble models is higher than the individual deep learners and the dermatologists’ diagnosis accuracy. The proposed ensemble models are compared with the recently developed skin cancer classification approaches. The results show that the proposed ensemble models outperform recently developed multiclass skin cancer classification models.

Uncertainty Quantification in Skin Cancer Classification using Three-Way Decision-based Bayesian Deep Learning

2021 ◽  
pp. 104418
Author(s):  
Moloud Abdar ◽  
Maryam Samami ◽  
Sajjad Dehghani Mahmoodabad ◽  
Thang Doan ◽  
Bogdan Mazoure ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Uncertainty Quantification ◽  
Cancer Classification

scholarly journals Classification of the Trap-Neuter-Return Surgery Images of Stray Animals Using Yolo-Based Deep Learning Integrated with a Majority Voting System

2021 ◽  
Vol 11 (18) ◽  
pp. 8578
Author(s):  
Yi-Cheng Huang ◽  
Ting-Hsueh Chuang ◽  
Yeong-Lin Lai
Keyword(s):  
Deep Learning ◽  
Majority Voting ◽  
Effective Solution ◽  
Stray Cats ◽  
Average Accuracy ◽  
Voting System ◽  
The Cost ◽  
Animal Care ◽  
Manual Review

Trap-neuter-return (TNR) has become an effective solution to reduce the prevalence of stray animals. Due to the non-culling policy for stray cats and dogs since 2017, there is a great demand for the sterilization of cats and dogs in Taiwan. In 2020, Heart of Taiwan Animal Care (HOTAC) had more than 32,000 cases of neutered cats and dogs. HOTAC needs to take pictures to record the ears and excised organs of each neutered cat or dog from different veterinary hospitals. The correctness of the archived medical photos and the different shooting and imaging angles from different veterinary hospitals must be carefully reviewed by human professionals. To reduce the cost of manual review, Yolo’s ensemble learning based on deep learning and a majority voting system can effectively identify TNR surgical images, save 80% of the labor force, and its average accuracy (mAP) exceeds 90%. The best feature extraction based on the Yolo model is Yolov4, whose mAP reaches 91.99%, and the result is integrated into the voting classification. Experimental results show that compared with the previous manual work, it can decrease the workload by more than 80%.

scholarly journals Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

2020 ◽  
Vol 7 ◽  
Author(s):  
Achim Hekler ◽  
Jakob N. Kather ◽  
Eva Krieghoff-Henning ◽  
Jochen S. Utikal ◽  
Friedegund Meier ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Classification ◽  
Label Noise

scholarly journals Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Long Zhang ◽  
Hong Jie Gao ◽  
Jianhua Zhang ◽  
Benjamin Badami
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Network Models ◽  
Reduce Mortality Rate ◽  
System Diagnosis ◽  
Digital Database ◽  
Whale Optimization ◽  
Diagnosis Accuracy

AbstractConvolutional neural networks (CNNs) are a branch of deep learning which have been turned into one of the popular methods in different applications, especially medical imaging. One of the significant applications in this category is to help specialists make an early detection of skin cancer in dermoscopy and can reduce mortality rate. However, there are a lot of reasons that affect system diagnosis accuracy. In recent years, the utilization of computer-aided technology for this purpose has been turned into an interesting category for scientists. In this research, a meta-heuristic optimized CNN classifier is applied for pre-trained network models for visual datasets with the purpose of classifying skin cancer images. However there are different methods about optimizing the learning step of neural networks, and there are few studies about the deep learning based neural networks and their applications. In the present work, a new approach based on whale optimization algorithm is utilized for optimizing the weight and biases in the CNN models. The new method is then compared with 10 popular classifiers on two skin cancer datasets including DermIS Digital Database Dermquest Database. Experimental results show that the use of this optimized method performs with better accuracy than other classification methods.

An efficient technique for skin cancer classification using deep learning

2020 ◽  
Author(s):  
Rehan Ashraf ◽  
Iqra Kiran ◽  
Toqeer Mahmood ◽  
Ateeq Ur Rehman Butt ◽  
Nafeesa Razzaq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Classification ◽  
Efficient Technique

scholarly journals Skin Cancer Classification Using Convolutional Neural Networks with Integrated Patient Data: A Systematic Review (Preprint)

2020 ◽  
Author(s):  
Julia Höhn ◽  
Achim Hekler ◽  
Eva Krieghoff-Henning ◽  
Jakob Nikolas Kather ◽  
Jochen Sven Utikal ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Data Integration ◽  
Image Features ◽  
Patient Data ◽  
Cancer Classification ◽  
Classification Tasks ◽  
The Impact

BACKGROUND In the past years, accuracy of skin cancer classification by convolutional neural networks (CNNs) has improved substantially. On classification tasks of single images, CNNs have performed on par or better than dermatologists. However, in clinical practice dermatologists also use other patient data beyond the visual aspects present in a digitized image which increases their diagnostic accuracy. The effect of integration of different subtypes of patient data into CNN-based skin cancer classifiers was recently investigated in several pilot studies. OBJECTIVE This systematic review focuses on current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. The aim is to explore the potential in this field of research by evaluating the type of patient data used, the ways the non-image data is encoded and merged with the image features as well as the impact of the integration for the classifier performance. METHODS Google Scholar, PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published in English dealing with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information and patient data were combined. RESULTS A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex and lesion location. Patient data was mostly one-hot encoded. Differences occur in the complexity that the encoded patient data was processed with regarding deep learning methods before and after fusing it with the image features for a ‘combined classifier’. CONCLUSIONS The present studies indicate a potential benefit of patient data integration into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhances classification performance, especially in case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for the benefit of the patient.

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