Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks

Popescu, Dan; Elkhatib, Mohamed; Ichim, Loretta

doi:10.3390/s22124399

Cited by 42 publications

(25 citation statements)

References 27 publications

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“…Step 3: Establish wolf pack habitats based on three wolves α, β, and δ. Individuals ranked in the bottom 5% in terms of fitness will be relocated or extirpated using Equation (11).…”

Section: Adaptive Weight Average Ensemble Strategymentioning

confidence: 99%

An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

Liu,

Zhang,

2024

Int J Imaging Syst Tech

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Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL‐DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. In general, the results indicate that the proposed method achieves high accuracy and practicality in skin lesion classification. Our model shows excellent performance in comparison with other existing models, which makes it significant for research and application in dermatology diagnosis.

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“…Step 3: Establish wolf pack habitats based on three wolves α, β, and δ. Individuals ranked in the bottom 5% in terms of fitness will be relocated or extirpated using Equation (11).…”

Section: Adaptive Weight Average Ensemble Strategymentioning

confidence: 99%

An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

Liu,

Zhang,

2024

Int J Imaging Syst Tech

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“…The work, however, revealed limitations concerning asymmetrical boundaries in terms of identification. The article [17] suggested an approach for a skin lesion classification system based on deep learning techniques and collective intelligence system (CIS), which involves multiple convolutional neural networks (CNNs), trained on the HAM10000 dataset, which is able to predict seven skin lesions including melanoma. Article [18] suggests an approach for multi class skin lesion classification and detection by teledermatology.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• Visual representation of contextually aware semantic segmentation using gradient activation maps [17] 86.71 16-layered CNN model and HDCT [18] 88.39 Proposed 89.21 the contextually aware mole separator's capabilities.…”

Section: • Analysis Of the Proposed Texture Encodermentioning

confidence: 99%

A Novel Skin and Mole Pattern Identification Using Deep Residual Pooling Network (DRPN)

2023

IJIES

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The process of identifying a criminal is extremely challenging if no eyewitnesses are available or closedcircuit television (CCTV) footage does not show the criminal's face. With the use of high-definition cameras and soft biometrics, forensic science has made great strides in identifying criminals based on their bodily traits. Due to the use of one type of soft biometric for identification, many researchers have focused on this area and have failed to achieve better matching. Despite the fact that similar soft biometrics share many of the same features, one type of soft biometric matching has been insufficient and resulted in inaccurate results. As a result, it becomes difficult to make distinct classifications. Therefore, this study proposes the implementation of a suspect identification system that uses mole pattern analysis and skin tone matching as soft biometrics. The deep residual pooling network is employed to analyze mole patterns, while the histogram equalization method is employed to analyze skin tones. We have also improved the performance of the deep seg-net and the histogram equalization technique by incorporating an aggregator operator and an adaptive pixel-wise noise cancellation filter. The experimental results show that the proposed method attains higher accuracy when the loss rate is gradually decreased. The proposed method attains 89.21% accuracy which is higher than the existing approaches and the efficiency of the proposed method is compared with various texture encoders.

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“…Popescu et al [ 22 ] presented a system based on the deep learning methodology and collective intelligence. Various CNN-based models were employed on the HAM10000 dataset, which can differentiate skin lesions, including melanoma.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

et al. 2022

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Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

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Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks

Cited by 42 publications

References 27 publications

An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

A Novel Skin and Mole Pattern Identification Using Deep Residual Pooling Network (DRPN)

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

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