Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search
Abstract
:1. Introduction
- A pre-trained deep learning is used to learn and extract new representations for skin cancer images.
- A novel FS algorithm is proposed to reduce the dimensionality of extracted features and improve the overall performance by determining the relevant features.
- Two real-world datasets are used to validate and compare the proposed method to well-known methods.
- A more general framework is suggested to integrate the proposed method into the system.
2. Related Works
2.1. Deep Learning-Based Medical Images
2.2. Medical Images Classification Using FS Optimizers
3. Background
3.1. Efficient Neural Networks
3.2. Hunger Games Search
Algorithm 1 Steps of HGS |
|
3.3. Particle Swarm Optimization
Algorithm 2 Algorithm of PSO |
|
3.4. Dynamic-Opposite Learning
4. Proposed Model
4.1. Deep Learning for Feature Extraction
- (1)
- Replacing the two last output layers in MobileNetV3 with dense connected blocks including two convolutions for feature extraction and classification, respectively;
- (2)
- Fine-tuning the modified MobileNetV3 on the skin cancer dataset;
- (3)
- Extracting the corresponding feature vector of each image from the convolution layer added to the MobileNetV3 model; where the extracted features for each image are flattened into a vector of size 128.
- (4)
- Later, the extracted features for each image are fed to the feature selection part in our framework.
4.2. Steps of DOLHGS Feature Selection Algorithm
4.3. Framework of the Developed Skin Cancer Detection
5. Experiments and Results
5.1. Description of Datasets
5.2. Performance Measures
5.3. Results and Discussion
5.3.1. Comparison with FS Methods
5.3.2. Comparison with Previous Works
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Input | Operator | Output | SE | NL | Stride |
---|---|---|---|---|---|
2d-Conv | 16 | FALSE | HS | 2 | |
16 | FALSE | RE | 1 | ||
24 | FALSE | RE | 2 | ||
24 | FALSE | RE | 1 | ||
40 | TRUE | RE | 2 | ||
40 | TRUE | RE | 1 | ||
40 | TRUE | RE | 1 | ||
80 | FALSE | HS | 2 | ||
80 | FALSE | HS | 1 | ||
80 | FALSE | HS | 1 | ||
80 | FALSE | HS | 1 | ||
112 | TRUE | HS | 1 | ||
112 | TRUE | HS | 1 | ||
160 | TRUE | HS | 2 | ||
160 | TRUE | HS | 1 | ||
160 | TRUE | HS | 1 | ||
2d-Conv | 960 | FALSE | HS | 1 | |
Adaptive average pooling | 960 | FALSE | - | 1 | |
Image embedding | 128 | FALSE | HS | 1 |
Dataset | Skin Disease | # Training Images | # Testing Images | Total Images per Category |
---|---|---|---|---|
ISIC-2016 | Malignant | 173 | 75 | 248 |
Benign | 727 | 304 | 1031 | |
Total images | 900 | 379 | 1279 | |
Common Nevus | 68 | 12 | 80 | |
Atypical Nevus | 68 | 12 | 80 | |
Melanoma | 34 | 6 | 40 | |
Total images | 170 | 30 | 200 |
Algorithm | Value of the Parameters |
---|---|
DOLHGS | EPSILON = 10 × 10, MIN-PROB = 0, MAX-PROB = −1 |
WOA | a = 2 to 0, a2 = −1 to −2 |
BAT | QMin = 0, QMax = 2 |
MVO | WEPMax = 1, WEPMin = 0.2 |
PSO | VMax = 6, WMax = 0.9, WMin = 0.2 |
FFA | Alpha = 0.5, BetaMin = 0.2, Gamma = 1 |
HGS | EPSILON = 10 × 10, POS = 0, F IT = 1 |
ISIC | PH2 | |||||||
---|---|---|---|---|---|---|---|---|
AC | R | P | F1 | AC | R | P | F1 | |
PSO | 0.865699 | 0.865699 | 0.856919 | 0.852251 | 0.956429 | 0.956429 | 0.956949 | 0.956522 |
MVO | 0.863325 | 0.863325 | 0.853915 | 0.849824 | 0.956071 | 0.956071 | 0.956575 | 0.956165 |
WOA | 0.86781 | 0.86781 | 0.860512 | 0.853141 | 0.957143 | 0.957143 | 0.957592 | 0.957233 |
FFA | 0.865435 | 0.865435 | 0.857003 | 0.85143 | 0.956429 | 0.956429 | 0.956918 | 0.956521 |
BAT | 0.867018 | 0.867018 | 0.860102 | 0.851955 | 0.956071 | 0.956071 | 0.956581 | 0.956165 |
HGS | 0.864908 | 0.864908 | 0.85652 | 0.850973 | 0.956429 | 0.956429 | 0.95694 | 0.956513 |
DOLHGS | 0.88185 | 0.87517 | 0.87633 | 0.87575 | 0.96429 | 0.97429 | 0.97699 | 0.97563 |
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Share and Cite
Dahou, A.; Aseeri, A.O.; Mabrouk, A.; Ibrahim, R.A.; Al-Betar, M.A.; Elaziz, M.A. Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search. Diagnostics 2023, 13, 1579. https://doi.org/10.3390/diagnostics13091579
Dahou A, Aseeri AO, Mabrouk A, Ibrahim RA, Al-Betar MA, Elaziz MA. Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search. Diagnostics. 2023; 13(9):1579. https://doi.org/10.3390/diagnostics13091579
Chicago/Turabian StyleDahou, Abdelghani, Ahmad O. Aseeri, Alhassan Mabrouk, Rehab Ali Ibrahim, Mohammed Azmi Al-Betar, and Mohamed Abd Elaziz. 2023. "Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search" Diagnostics 13, no. 9: 1579. https://doi.org/10.3390/diagnostics13091579