Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning model

Sabri, My Abdelouahed; Filali, Youssef; Khoukhi, Hasnae El; Aarab, Abdellah

doi:10.1109/iscv49265.2020.9204324

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…In recent years, the applications of Artificial Intelligence (AI) in various fields have developed rapidly, especially in the fields of medical image analysis and bioinformatics. At present, AI is widely used in skin cancer diagnosis ( 19 – 21 ). From the point of view of whether features can be extracted automatically, the AI approaches in this area can be divided into skin cancer classification methods based on machine learning and skin cancer classification methods based on deep learning.…”

Section: Related Workmentioning

confidence: 99%

“…Support Vector Machine (SVM) algorithms with polynomial kernels provide better accuracy than other machine learning algorithms, such as decision trees using Gini indices and entropy, naive Bayes classifiers, extreme gradient boosting (XGBoost) classifiers, random forests, and logistic regression algorithms. Sabri ( 19 ) first extracted the shapes, colors, textures and skeletons of skin image lesions, then used the information gain method to determine the best combination of features, and finally input this feature combination into a commonly used machine learning algorithm to predict the categories of legions. Vidya ( 27 ) first extracted skin image asymmetry, border, color, and diameter information.…”

Section: Related Workmentioning

confidence: 99%

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Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion

Wan

Chen

et al. 2022

Front. Public Health

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Pigmented skin disease is caused by abnormal melanocyte and melanin production, which can be induced by genetic and environmental factors. It is also common among the various types of skin diseases. The timely and accurate diagnosis of pigmented skin disease is important for reducing mortality. Patients with pigmented dermatosis are generally diagnosed by a dermatologist through dermatoscopy. However, due to the current shortage of experts, this approach cannot meet the needs of the population, so a computer-aided system would help to diagnose skin lesions in remote areas containing insufficient experts. This paper proposes an algorithm based on a fusion network for the detection of pigmented skin disease. First, we preprocess the images in the acquired dataset, and then we perform image flipping and image style transfer to augment the images to alleviate the imbalance between the various categories in the dataset. Finally, two feature-level fusion optimization schemes based on deep features are compared with a classifier-level fusion scheme based on a classification layer to effectively determine the best fusion strategy for satisfying the pigmented skin disease detection requirements. Gradient-weighted Class Activation Mapping (Grad_CAM) and Grad_CAM++ are used for visualization purposes to verify the effectiveness of the proposed fusion network. The results show that compared with those of the traditional detection algorithm for pigmented skin disease, the accuracy and Area Under Curve (AUC) of the method in this paper reach 92.1 and 95.3%, respectively. The evaluation indices are greatly improved, proving the adaptability and accuracy of the proposed method. The proposed method can assist clinicians in screening and diagnosing pigmented skin disease and is suitable for real-world applications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion

Wan

Chen

et al. 2022

Front. Public Health

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“…Its a general approach in meta‐machine learning. Many research findings used EL to merge the diagnosis results of numerous methods and had shown improved classification results (Gessert et al, 2020; Sabri et al, 2020). Shahin et al (2018) designed a deep EL approach using ResNet50 and Inception V3 to classify seven types of skin lesions.…”

Section: Related Workmentioning

confidence: 99%

M²CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

Gajera,

Nayak,

Zaveri

2023

Expert Systems

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Due to inter‐class homogeneity and intra‐class variability, the classification of skin lesions in dermoscopy images has remained difficult. Although deep convolutional neural networks (DCNNs) have achieved satisfactory performance for binary skin cancer classification, multiclass skin lesion classification is still an open problem due to the limited training samples and class imbalance issues. To tackle these issues, in this article, we propose a multi‐CNN ensemble approach dubbed for multiclass skin lesion classification. The includes three individual CNN models, each helping in extracting different high‐level features from skin images and thereby yielding different prediction results. First, we design a lightweight CNN model to extract prominent features and train it from scratch, which primarily aims at avoiding the data scarcity problem. Then, we ensemble two different pre‐trained CNN models with the lightweight model to improve the performance and generalization capability. The proposed ensemble approach can effectively fuse the predictions of each individual CNN model using the averaging method. The approach is validated using a benchmark data set, HAM10000, which contains skin lesion images of seven different classes. The results demonstrate that the outperforms base CNN models and state‐of‐the‐art approaches without using any external data.

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“…The function used is the best combination of functions extracted from different functions. H. Shape, color, texture, skeleton Lesions and then use different algorithms to classify these features Predict the class [35]. Another article proposes image processing techniques to classify skin lesions into melanoma or moles.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesions Detection and Classification Using Deep Learning

2021

IJATCSE

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The skin lesion is considered the most widespread malignant disease in individuals, and melanoma is the deadliest form of the disease. Early detection affects the prognosis of the disease and improves the chances of survival. Dermatologists use scientific calculation tools such as ABCD to diagnose melanoma through visual inspection of the mole. However, computer vision tools have been presented to support the quantitative scrutiny of skin lesions. Significant improvements to deep learning algorithms in image recognition tasks should be very successful in medical image examination, particularly in the classification of skin lesions used to diagnose melanoma. In this research, a deep learning simulation with 38 layers to detect and classify skin lesions was proposed. Two datasets were used for training and testing i.e., the HAM10000 dataset & the ISIC2019 dataset. Experimental results show that the model outperforms on both the datasets hence making it nondependent of the dataset. 94.45% of validation top 3 accuracies are achieved on the HAM10000 dataset & 93.06% of validation top 3 accuracies are achieved on the ISIC2019 dataset.

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Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning model

Cited by 17 publications

References 9 publications

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion

M²CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

Skin Lesions Detection and Classification Using Deep Learning

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Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning model

Cited by 17 publications

References 9 publications

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion

M2CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

Skin Lesions Detection and Classification Using Deep Learning

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Product

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M²CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion