Skin lesion classification based on the <scp>VGG</scp>‐16 fusion residual structure

Yan, Penghui; Wang, Gang; Chen, Jie; Tang, Qingwei; Xu, Heng

doi:10.1002/ima.22798

Cited by 4 publications

(5 citation statements)

References 32 publications

(44 reference statements)

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“…In this work, we provided a brief overview of the two DNN models, 3D-VGG-16 and ResNet3D, that in previous studies also showed success in analysing images from skin lesions to coronavirus disease of 2019 (COVID-19) infection-affected lungs [32,33]. However, while most health pathologies have typical symptomatics and it is easier to automatically group them [34], it is not the case in neurodegenerative diseases, such as AD [35].…”

Section: Discussionmentioning

confidence: 99%

Alzheimer’s disease detection from magnetic resonance imaging: a deep learning perspective

Armonaite

Ventura

Laura

2023

Explor Neuroprot Ther

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Aim: Up to date many successful attempts to identify various types of lesions with machine learning (ML) were made, however, the recognition of Alzheimer’s disease (AD) from brain images and interpretation of the models is still a topic for the research. Here, using AD Imaging Initiative (ADNI) structural magnetic resonance imaging (MRI) brain images, the scope of this work was to find an optimal artificial neural network architecture for multiclass classification in AD, circumventing the dozens of images pre-processing steps and avoiding to increase the computational complexity. Methods: For this analysis, two supervised deep neural network (DNN) models were used, a three-dimensional 16-layer visual geometry-group (3D-VGG-16) standard convolutional network (CNN) and a three-dimensional residual network (ResNet3D) on the T1-weighted, 1.5 T ADNI MRI brain images that were divided into three groups: cognitively normal (CN), mild cognitive impairment (MCI), and AD. The minimal pre-processing procedure of the images was applied before training the two networks. Results: Results achieved suggest, that the network ResNet3D has a better performance in class prediction, which is higher than 90% in training set accuracy and arrives to 85% in validation set accuracy. ResNet3D also showed requiring less computational power than the 3D-VGG-16 network. The emphasis is also given to the fact that this result was achieved from raw images, applying minimal image preparation for the network. Conclusions: In this work, it has been shown that ResNet3D might have superiority over the other CNN models in the ability to classify high-complexity images. The prospective stands in doing a step further in creating an expert system based on residual DNNs for better brain image classification performance in AD detection.

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Section: Discussionmentioning

confidence: 99%

Alzheimer’s disease detection from magnetic resonance imaging: a deep learning perspective

Armonaite

Ventura

Laura

2023

Explor Neuroprot Ther

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“…To tackle the challenge of a small dataset size, the utilization of pretrained models in end-to-end systems was chosen, along with their use as feature extractors in conjunction with traditional ML classifiers. The selection of these models was substantiated by evidence from published sources [15,[46][47][48][49][50].…”

Section: Methodsmentioning

confidence: 99%

“…In the machine-learning module, SVM, k-NN, LR, and RF were selected due to their versatility in handling diverse data types and their robust performance in classification tasks, including skin lesions, as documented in the literature [15,[46][47][48][49][50]. SVM's adaptability to both linear and nonlinear tasks, along with its effectiveness in highdimensional spaces, aligns well with the complex nature of skin lesion classification.…”

Section: Modulementioning

confidence: 99%

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

Suleiman,

Anyimadu,

Permana

et al. 2024

Vis. Comput. Ind. Biomed. Art

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Skin lesion classification plays a crucial role in the early detection and diagnosis of various skin conditions. Recent advances in computer-aided diagnostic techniques have been instrumental in timely intervention, thereby improving patient outcomes, particularly in rural communities lacking specialized expertise. Despite the widespread adoption of convolutional neural networks (CNNs) in skin disease detection, their effectiveness has been hindered by the limited size and data imbalance of publicly accessible skin lesion datasets. In this context, a two-step hierarchical binary classification approach is proposed utilizing hybrid machine and deep learning (DL) techniques. Experiments conducted on the International Skin Imaging Collaboration (ISIC 2017) dataset demonstrate the effectiveness of the hierarchical approach in handling large class imbalances. Specifically, employing DenseNet121 (DNET) as a feature extractor and random forest (RF) as a classifier yielded the most promising results, achieving a balanced multiclass accuracy (BMA) of 91.07% compared to the pure deep-learning model (end-to-end DNET) with a BMA of 88.66%. The RF ensemble exhibited significantly greater efficiency than other machine-learning classifiers in aiding DL to address the challenge of learning with limited data. Furthermore, the implemented predictive hybrid hierarchical model demonstrated enhanced performance while significantly reducing computational time, indicating its potential efficiency in real-world applications for the classification of skin lesions.

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“…Based on the literature [ 30 ], we select 70% of the data as the basic training set, 20% of the data as the basic test set, and the remainder of the data as the basic verification set, and the number of each lesion in the training set, test set, and verification sets is divided into a ratio of 7 : 2 : 1. The sample distribution after dataset division is shown in Table 1 Hair removal [ 31 ]: the ISIC2018 datasets are often characterized by hair-like regions within the skin lesions, which would interfere with the model's extraction of pathological features. Thus, we dehair all images to reduce the hair interfere.…”

Section: Methodsmentioning

confidence: 99%

“…Hair removal [ 31 ]: the ISIC2018 datasets are often characterized by hair-like regions within the skin lesions, which would interfere with the model's extraction of pathological features. Thus, we dehair all images to reduce the hair interfere.…”

Section: Methodsmentioning

confidence: 99%

Multiscale Feature Fusion for Skin Lesion Classification

Wang

Yan

Tang

et al. 2023

BioMed Research International

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Skin cancer has a high mortality rate, and early detection can greatly reduce patient mortality. Convolutional neural network (CNN) has been widely applied in the field of computer-aided diagnosis. To improve the ability of convolutional neural networks to accurately classify skin lesions, we propose a multiscale feature fusion model for skin lesion classification. We use a two-stream network, which are a densely connected network (DenseNet-121) and improved visual geometry group network (VGG-16). In the feature fusion module, we construct multireceptive fields to obtain multiscale pathological information and use generalized mean pooling (GeM pooling) to reduce the spatial dimensionality of lesion features. Finally, we built and tested a system with the developed skin lesion classification model. The experiments were performed on the dataset ISIC2018, which can achieve a good classification performance with a test accuracy of 91.24% and macroaverages of 95%.

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Skin lesion classification based on the VGG‐16 fusion residual structure

Cited by 4 publications

References 32 publications

Alzheimer’s disease detection from magnetic resonance imaging: a deep learning perspective

Alzheimer’s disease detection from magnetic resonance imaging: a deep learning perspective

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

Multiscale Feature Fusion for Skin Lesion Classification

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