A new color assessment methodology using cluster-based features for skin lesion analysis

Vasconcelos, Maria João M.; Rosado, Luís; Ferreira, Márcia

doi:10.1109/mipro.2015.7160298

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…To further validate our model’s merit, we compared it with studies utilizing traditional machine learning algorithms. Vasconcelos et al [ 33 ]’s color evaluation method, which incorporated feature selection and machine learning classification, achieved identification accuracies of 0.7775 and 0.8138 on two smaller public datasets. Similarly, Ohki et al [ 34 ] employed the traditional RF classifier model, reaching a sensitivity and specificity of 0.7980 and 0.8070 on 1148 skin images.…”

Section: Discussionmentioning

confidence: 99%

An Actinic Keratosis Auxiliary Diagnosis Method Based on an Enhanced MobileNet Model

Liu

et al. 2023

Bioengineering

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Actinic keratosis (AK) is a common precancerous skin lesion with significant harm, and it is often confused with non-actinic keratoses (NAK). At present, the diagnosis of AK mainly depends on clinical experience and histopathology. Due to the high difficulty of diagnosis and easy confusion with other diseases, this article aims to develop a convolutional neural network that can efficiently, accurately, and automatically diagnose AK. This article improves the MobileNet model and uses the AK and NAK images in the HAM10000 dataset for training and testing after data preprocessing, and we performed external independent testing using a separate dataset to validate our preprocessing approach and to demonstrate the performance and generalization capability of our model. It further compares common deep learning models in the field of skin diseases (including the original MobileNet, ResNet, GoogleNet, EfficientNet, and Xception). The results show that the improved MobileNet has achieved 0.9265 in accuracy and 0.97 in Area Under the ROC Curve (AUC), which is the best among the comparison models. At the same time, it has the shortest training time, and the total time of five-fold cross-validation on local devices only takes 821.7 s. Local experiments show that the method proposed in this article has high accuracy and stability in diagnosing AK. Our method will help doctors diagnose AK more efficiently and accurately, allowing patients to receive timely diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

An Actinic Keratosis Auxiliary Diagnosis Method Based on an Enhanced MobileNet Model

Liu

et al. 2023

Bioengineering

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“…Additionally, we compared the result of AK-DL with other studies using traditional machine learning algorithms, and it also supported the superiority of the AK-DL model. Vasconcelos et al [31] proposed a new color evaluation method and then applied feature selection and machine learning classification, resulting in an identification accuracy of 0.7775 and 0.8138 on two smaller public datasets, respectively. Ohki et al [32] used the RF model, a traditional classifier, to automatically classify 1148 skin images (including 980 melanocytic nevi and 168 melanomas) and acquired sensitivity of 0.7980 and specificity of 0.8070 after taking a 10-fold cross-validation.…”

Section: Discussionmentioning

confidence: 99%

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

et al. 2020

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Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.

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“…This research proposed a method for color feature evaluation of lesion images using two datasets of dermoscopic images and one mobile-acquired dataset, which provided accuracy rates of 77.75%, 81.38%, and 93.55%, respectively. Although the methodology is successful, there are limitations, such as the reliance on machine learning and the need for broader and improved usability [12,13]. This work presented a persistent learning framework for training deep neural networks.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer

Abdullah,

Siddique,

Shaukat

et al. 2024

Diagnostics

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Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.

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A new color assessment methodology using cluster-based features for skin lesion analysis

Cited by 11 publications

References 14 publications

An Actinic Keratosis Auxiliary Diagnosis Method Based on an Enhanced MobileNet Model

An Actinic Keratosis Auxiliary Diagnosis Method Based on an Enhanced MobileNet Model

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer

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