Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification
Abstract
:1. Introduction
1.1. Research Highlights
- We used cross validation to test our model and the results showed that it worked much better than the methods already being used.
- This study proposes a new classification model (Assist-Dermo) to recognize multiclass PSLs.
- A new preprocessing step is integrated into the perceptual-oriented color space to enhance contrast and adjust the light.
- The proposed Assist-Dermo model is constructed with many layers and various filter sizes but fewer filters, and these parameters are selected by using lightweight SqueezeNet on a depthwise separable CNN.
- It is assessed using experimental findings from the many datasets that were gathered with regards to sensitivity, specificity, and other metrics.
- The Assist-Dermo can reduce overfitting because the dense connection better protects against the overfitting problem, especially when learning from small amounts of data.
1.2. Research Outline
2. Review of Related Research
3. Materials and Methods
3.1. Acquisition and Preparation of Dataset
3.2. Preprocessing
3.3. Architecture of SqueezeNet-Light
Algorithm 1: ShuffleNet-Light Architecture for Features Extraction and Classification of PSLs |
Input: Input Tensor (), 2-D of (256 × 256 × 3) PSLs training dataset. Output: Obtained and Classified feature mapaugmented 2-D image Main Process: Step 1. Define number of stages = 4 Step 2. Iterate for Each Stage
Step 4. Fcat(i) = concatenation (# features-maps) Step 5. channel = shuffle (x) [End Step 2] Step 6. Model Construction
Step 8. Test samples are predicted to the class label using the decision function of the below equation. |
3.3.1. SqueezeNet Neural Network
- (1)
- To decrease network parameters, a smaller 1 × 1 convolution kernel is used in place of the 3 × 3 convolution (Conv) kernel;
- (2)
- The parameters and model volume are reduced using SqueezeNet’s developed fire layer;
- (3)
- A thinner (a smaller number of) pooling layer resulted; only three maxpooling levels and a global pooling-layer are present in SqueezeNet.
3.3.2. Depthwise Separable Convolution
3.3.3. Network Parameter Optimization
4. Experimental Results
4.1. Experimental Setup
4.2. Model Evaluation Metrics
4.3. Network Settings
4.4. Computational Cost
4.5. Visual Feature Representation
4.6. Performance of Proposed System
4.7. Comparisons with SOTA
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ref. | Diagnosis | Segment? | Classification | DL Model | Datasets | Result (%) ** | Limitations |
---|---|---|---|---|---|---|---|
[9] | Proposed a hybrid Inception adaptive-neuro-fuzzy (ANF) model for discriminating dermoscopic photos into different seven labels. | Yes | 7 classes | Inception-v4 | ISIC-2018 | ACC: 97.91% SE: 93.4% SP: 98.7% | Classes imbalance problem, evaluated on single dataset, classifies only seven classes, and computationally expensive. |
[10] | Suggested a hybrid ResNet-SVM framework for efficient binary classification of skin lesions. | No | Binary | ResNet50, VGG-16 and SVM | ISIC 2017 ISBI 2016 | ACC: 99.19% | Classes imbalance problem, binary classification only two classes, and computationally expensive. |
[15] | Offered an augmented ROI -based CNN system to recognize and separate melanoma from nevus malignancy. | Yes | Binary | CNN + Transfer learning | DermIS, DermQuest | ACC: 97.9% ACC: 97.4% | Classes imbalance problem, evaluated on single dataset, classifies only two classes, and computationally expensive. |
[17] | Introduced a six-class cutaneous lesions classification system based on CNN. | No | 6 classes | Faster region-based CNN (FRCNN) | Private (5846 images) | ACC: Six-classes 86.2% Two-classes 91.5% | Image processing, handcrafted-based feature extraction approach, which limits the detection accuracy, 6 classes only, and computationally expensive |
[19] | Suggested a hybrid ResNet-SVM framework for efficient binary classification of skin lesions. | No | Binary | SqueezeNet, DenseNet, inception v3 and ResNet | HAM10000 | AUC: 0.997 | Evaluated on one dataset, classifies only two classes, and computationally expensive. |
[20] | Implemented a CNN model with many layers, various filter sizes, lower number of filters and settings for skin cancer categorization. | No | 3 classes | DCNN | ISIC 2017 ISIC 2018 ISIC 2019 | AUC: 0.964 | Three classes of PSLs and reduced hyper-parameters, so computationally expensive. |
[25] | Suggested a hybrid-CNN made up of three different feature extracting modules that were combined to produce lesion feature vectors with better depth. | Yes | 7 classes | CNN | ISIC-2016, ISIC-2017, ISIC-2018 | AUC: ISIC-2016: 0.96 ISIC-2017: 0.95 ISIC-2018: 0.97 | Seven classes only and used only one limited dataset, classifier is not generalized. |
[26] | Presented the classification of four different forms of skin cancer using the SCDNet, a vgg16-based framework. | No | 4 classes | Vgg16 | ISIC 2019 | ACC: 96.91% | Four classes only, many hyper-parameters required, and tested on signal dataset, so classifier is not generalized. |
[29] | Proposed a new regularization technique for CNN model to binary classify skin lesions. | No | Binary | CNN | ISIC-2018 | ACC: 97.49% | Two classes (benign vs. malignant), required huge parameters, evaluate on single dataset, and computationally expensive. |
[31] | Examined how well three of the best pretrained DL models classified skin cancer in binary form. | No | Binary | ResNet, VGG16, MobileNetV2 | ISIC 2020 | ACC: 98.39% | Two classes (benign vs. malignant), required huge parameters, evaluate on single dataset, and computationally expensive. |
[34] | Used transfer learning and a pre-trained deep learning architecture to classify three skin lesions. | No | 3 classes | AlexNet | ph2 | Acc: 98.61% SE: 98.33% SP: 98.93% | Three classes only, required huge parameters, evaluate on single dataset, and computationally expensive. |
[35] | Described the creation of an ensemble of deep CNNs to further improve the efficiency of each CNN while identifying dermoscopy photos into three categories | No | 3 classes | GoogLeNet, AlexNet, ResNet, VGGNet | ISBI 2017 | AUC: 0.891 | Single dataset used, three classes of PSLs and reduced hyper-parameters, so computationally expensive. |
[36] | Suggested an architecture based on weighted mean ensemble learning to categorize seven different kinds of skin infections | No | 7 classes | ResNeXt, SeResNeXt, DenseNet, Xception, ResNet | HAM10000 ISIC 2019 | ACC/recall: avg.: 87%/93% weight avg.: 88%/94% | Seven classes with two datasets used, three classes of PSLs and reduced hyper-parameters, so computationally expensive. |
[37] | Employed 4867 clinical photos from 1842 patients who had been diagnosed with skin tumors as a dataset to train a DCNN architecture. | No | Binary | DCNN | Private (4867 images) | ACC: 92.4% | Two classes (benign vs. malignant), required huge parameters, evaluate on single dataset, and computationally expensive. |
[38] | a bilinear CNN strategy that made use of transfer learning, a soft-adjustment step, and data augmentation to enhance classification performance while lowering the computing cost. | No | 7 classes | ResNet50 and VGG16 | HAM10000 | ACC: 93.21% | Three classes only and used only one limited dataset, classifier is not generalized. |
[39] | classified dermoscopy images into seven classes comprising the advantage of binary support. | No | 7 classes | GoogLeNet and Inception-v3 | ISIC 2018 | BMA: 67.7% | Seven classes, required huge parameters, evaluate on single dataset, and computationally expensive. |
[41] | Deep learning’s efficiency to that of derma specialists in categorizing histopathologic melanoma photos. | No | Binary | CNN | Private (695 lesions) | ACC: 68% SE: 76% SP: 60% | Two classes (benign vs. malignant), required huge parameters, evaluate on single dataset, and computationally expensive. |
Dataset | Ref. | Images | Selected Images | Number of Classes * |
---|---|---|---|---|
HAM10000 | [32,33] | 10,015 | 10,015 | 7 (“AK, BCC, BKL, DF, NV, MEL, VASC”) |
ISIC 2019 | [42] | 25,331 | 25,331 | 8 (“MEL, NV, BCC, AKIEC, BKL, DF, VASC, SCC”) |
ISIC 2020 | [43] | 33,126 | 33,126 | 9 (“MEL, NV, BCC, AKIEC, BKL, DF, VASC, SCC, PBK”) |
Ph2 | [44] | 200 | 200 | 2(NV, and Mel) |
ISBI 2017 | [45] | 2750 | 2750 | 3 (NV, Mel, and SK) |
Classes * | No. of Images | Size |
---|---|---|
AK | 700 | (512,512,3) |
BCC | 3300 | (512,512,3) |
BKL | 2600 | (512,512,3) |
DF | 200 | (512,512,3) |
NV | 12,000 | (512,512,3) |
MEL | 4000 | (512,512,3) |
SCC | 600 | (512,512,3) |
VASC | 300 | (512,512,3) |
PBK | 300 | (512,512,3) |
Total | 24,000 | (512,512,3) |
Parameters | Angle | Brightness | Zoom | Shear | Mode | Horizontal | Vertical | Rescale | Noise |
---|---|---|---|---|---|---|---|---|---|
Values | 30° | [0.9, 1.1] | 0.1 | 0.1 | Constant | Flip | Flip | 1./255 | 0.45 |
Tensor Flow | GPU | Learning Rate | Optimizer | Number of Epoch | Batch size | Validation |
---|---|---|---|---|---|---|
2.9.1 | GeForce GTX 1050 Ti | 1 × 10−3 | AdaBelief | 40 | 16 | 10-fold |
Method | Preprocessing | Feature Extraction | Training | Prediction | Overall |
---|---|---|---|---|---|
VGG16 | 20.5 s | 14.4 s | 200.5 s | 10.8 s | 246.2 s |
AlexNet | 18.6 s | 12.2 s | 190.5 s | 8.8 s | 230.1 s |
InceptionV3 | 16.3 s | 14.8 s | 178.5 s | 7.8 s | 217.4 s |
GoogleNet | 17.2 s | 17.3 s | 170.5 s | 6.8 s | 211.8 s |
Xception | 18.1 s | 15.1 s | 165.5 s | 8.8 s | 207.5 s |
MobileNet | 14.1 s | 13.3 s | 160.5 s | 7.8 s | 195.7 s |
SqueezeNet | 10.8 s | 8.3 s | 168.5 s | 5.8 s | 193.4 s |
Proposed | 1.8 s | 1.9 s | 165.5 s | 1.5 s | 184.5 s |
Models | Image Size | Parameters | Validation Accuracy |
---|---|---|---|
VGG16 | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 14,714,688 14,865,222 14,911,302 | 79 |
AlexNet | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 23,587,712 14,911,302 | 81.3 |
InceptionV3 | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 42,658,176 14,911,302 | 82.7 |
GoogleNet | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 14,714,688 14,865,222 14,911,302 | 83.5 |
Xception | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 14,714,688 14,865,222 14,911,302 | 82.4 |
MobileNet | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 3,228,864 14,865,222 14,911,302 | 84.3 |
SqueezeNet | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 7,037,504 14,911,302 | 87.6 |
SqueezeNet-Light | 512 × 512 × 3 256 × 256 × 3 200 × 200 × 3 | 3,182,412 3,182,412 3,182,412 | 95.6 |
Model | Epochs | SE | SP | ACC | PR | F1-Score | MCC |
---|---|---|---|---|---|---|---|
VGG16 | 40 | 78 | 80 | 79 | 76 | 79 | 80 |
AlexNet | 40 | 79 | 82 | 81.3 | 80 | 80.4 | 81.1 |
InceptionV3 | 40 | 81 | 80 | 82.7 | 82 | 82.7 | 83.5 |
GoogleNet | 40 | 83 | 81 | 83.5 | 83 | 83.5 | 84.5 |
Xception | 40 | 82 | 83 | 82.4 | 83 | 84.3 | 85.4 |
MobileNet | 40 | 84 | 84.2 | 84.3 | 84 | 85.2 | 86.3 |
SqueezeNet | 40 | 85 | 86.2 | 87.6 | 85 | 86.1 | 87.2 |
Squeeze-Light | 40 | 94 | 96 | 95.6 | 94.12 | 95.2 | 96.7 |
Model | Epochs | SE | SP | ACC | PR | F1-Score | MCC |
---|---|---|---|---|---|---|---|
VGG16 | 40 | 78 | 80 | 79 | 76 | 79 | 80 |
AlexNet | 40 | 79 | 82 | 81.1 | 80 | 80.0 | 81.0 |
InceptionV3 | 40 | 81 | 80 | 82.3 | 82 | 82.2 | 83.4 |
GoogleNet | 40 | 83 | 81 | 83.6 | 83 | 83.3 | 84.3 |
Xception | 40 | 82 | 83 | 82.6 | 83 | 84.4 | 85.2 |
MobileNet | 40 | 84 | 84.0 | 84.3 | 84 | 85.1 | 86.1 |
SqueezeNet | 40 | 85 | 86.1 | 87.2 | 85 | 86.0 | 87.0 |
Squeeze-Light | 40 | 94 | 96 | 95.6 | 94.12 | 95.2 | 96.7 |
Model | Epochs | SE | SP | ACC | PR | F1-Score | MCC |
---|---|---|---|---|---|---|---|
VGG16 | 40 | 78 | 80 | 80 | 76 | 79 | 80.5 |
AlexNet | 40 | 80 | 81 | 80.3 | 80 | 80.4 | 82.3 |
InceptionV3 | 40 | 82 | 82 | 81.7 | 82 | 82.7 | 84.0 |
GoogleNet | 40 | 82 | 83 | 82.5 | 83 | 82.5 | 85.0 |
Xception | 40 | 84 | 84 | 83.4 | 83 | 83.3 | 86.0 |
MobileNet | 40 | 83 | 82.2 | 85.3 | 84 | 84.2 | 83.0 |
SqueezeNet | 40 | 85 | 85.2 | 86.6 | 85 | 85.1 | 86.1 |
Squeeze-Light | 40 | 94 | 96 | 95.6 | 94.12 | 95.2 | 96.7 |
Optimization | SE | SP | ACC | PR | F1-Score | MCC |
---|---|---|---|---|---|---|
SGD with Momentum | 80.2 | 81.8 | 82.9 | 85 | 80 | 82.2 |
Adam | 82 | 83 | 82.5 | 83 | 82.5 | 85.0 |
RMSProp | 84 | 84 | 83.4 | 83 | 83.3 | 86.0 |
AdaGrad | 83 | 82.2 | 85.3 | 84 | 84.2 | 83.0 |
AdaBelief | 94 | 96 | 95.6 | 94.12 | 95.2 | 96.7 |
Loss Functions | SE | SP | ACC | PR | F1-Score | MCC |
---|---|---|---|---|---|---|
Cross Entropy Loss | 82.2 | 97.8 | 96.9 | 76 | 79 | 82.2 |
Weighted Cross Entropy Loss | 94 | 96 | 95.6 | 94.12 | 95.2 | 96.7 |
DL Architectures | Complexity (MFLOPs) | Model Size (MB) | GPU Speed (S) |
---|---|---|---|
SqueezeNet-Light | 68.3 | 9.3 | 0.7 |
SqueezeNet | 96.9 | 14.5 | 1.6 |
MobileNet | 95.4 | 12.3 | 1.3 |
GoogleNet | 272.8 | 15.2 | 2.7 |
Xception | 281.8 | 16.3 | 2.6 |
InceptionV3 | 554.3 | 17.5 | 3.0 |
AlexNet | 65.9 | 14.5 | 2.8 |
VGG16 | 295.8 | 12.3 | 3.4 |
Batch Size | Epochs | CPU/TPU/GPU (mS) |
---|---|---|
64 | 40 | 700/500/400 |
128 | 40 | 750/400/500 |
256 | 40 | 750/400/500 |
512 | 40 | 750/400/500 |
1024 | 40 | 750/400/500 |
Model | Epochs | SE | SP | ACC | PR | F1-Score | Trainable Parameters |
---|---|---|---|---|---|---|---|
SqueezeNet-Light | 100 | 94 | 96 | 95.6 | 94.12 | 95.2 | 3,182,412 |
SqueezeNet | 100 | 87 | 85 | 87.6 | 87 | 87 | 7,037,504 |
Salama-ResNet-SVM [10] | 100 | 80 | 82 | 81 | 79 | 80 | 53,982,272 |
Ashraf-FRCNN [15] | 100 | 82 | 83 | 82 | 80 | 81 | 50,213,111 |
Naeem-VGG16 [26] | 100 | 83 | 85 | 83 | 83 | 84 | 48,222,341 |
Hosny-AlexNet [34] | 100 | 84 | 86 | 84 | 84 | 85 | 49,112,242 |
Fujisawa-DCNN [37] | 100 | 79 | 80 | 78 | 79 | 80 | 52,128,141 |
Harangi-GoogleNet-Inception [39] | 100 | 85 | 86 | 85 | 84 | 85 | 50,440,122 |
SOTA Architectures | Complexity (MFLOPs) | Parameters (M) | Model Size (MB) | GPU Speed (S) |
---|---|---|---|---|
SqueezeNet-Light | 68.3 | 20.18 | 9.3 | 0.7 |
SqueezeNet | 96.9 | 38.11 | 14.5 | 2.6 |
Salama-ResNet-SVM [10] | 120.3 | 53.98 | 22.3 | 2.7 |
Ashraf-FRCNN [15] | 140.9 | 50.21 | 20.5 | 5.6 |
Naeem-VGG16 [26] | 150.3 | 48.22 | 19.3 | 4.7 |
Hosny-AlexNet [34] | 122.3 | 49.11 | 17.3 | 4.7 |
Fujisawa-DCNN [37] | 120.9 | 52.13 | 16.5 | 3.6 |
Harangi-GoogleNet-Inception [39] | 150.3 | 50.44 | 19.3 | 3.7 |
TL Architectures | Limitations | Advantages |
---|---|---|
SqueezeNet-Light | It should be trained on more different datasets and this classifier can be tested on different modality of images to check the generalizability of the model. | Tiny model, high speed, several datasets are evaluated, and identifies nine classes |
SqueezeNet | It is small model but requires hyper-parameter tuning | It is better classifier compared to other pretrained TL models. |
Salama-ResNet-SVM [10] | Classes imbalance, binary classification only two classes, and computationally expensive. | Integration of SVM and ResNet and good for binary decision. |
Ashraf-FRCNN [15] | Classes imbalance, evaluated on single dataset, classify only two classes, and computationally expensive. | This approach used CNN and is better for features extraction. |
Naeem-VGG16 [26] | Four classes only, many hyper-parameters required, and tested on signal dataset so classifier is not generalized. | This method used pre-trained TL VG-16 to recognize four classes of PSLs |
Hosny-AlexNet [34] | Three classes only, required huge parameters, evaluate on single dataset, and computationally expensive. | This approach used CNN and is better for features extraction. |
Fujisawa-DCNN [37] | Two classes (benign vs. malignant), required huge parameters, evaluate on single dataset, and computationally expensive. | This approach used CNN and is better for features extraction. |
Harangi-GoogleNet-Inception [39] | Seven classes, required huge parameters, evaluate on single dataset, and computationally expensive. | Combining the GoogleNet and Inception pretrained TL to recognize nine classes. |
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Share and Cite
Abbas, Q.; Daadaa, Y.; Rashid, U.; Ibrahim, M.E.A. Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification. Diagnostics 2023, 13, 2531. https://doi.org/10.3390/diagnostics13152531
Abbas Q, Daadaa Y, Rashid U, Ibrahim MEA. Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification. Diagnostics. 2023; 13(15):2531. https://doi.org/10.3390/diagnostics13152531
Chicago/Turabian StyleAbbas, Qaisar, Yassine Daadaa, Umer Rashid, and Mostafa E. A. Ibrahim. 2023. "Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification" Diagnostics 13, no. 15: 2531. https://doi.org/10.3390/diagnostics13152531