Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing
Abstract
:1. Introduction
- Fog computing along with Cloud computing and IoT for real-time analysis and IoT monitoring system installation;
- An automatic, remote diagnosis of benign and malignant breast cancer in different people;
- A model for real-time breast cancer diagnosis using DTL was trained using images from mammograms;
- The predictive and network analysis performance of the proposed system is shown and analyzed;
- Predictive analytics by modeling and simulating IoT–Fog–Cloud environments;
- Introducing the findings and comparing them with prior research to emphasize the unique contribution of the current study.
2. Literature Study
3. Materials and Methods
3.1. Dataset Description and Acquisition
3.2. Methodologies
4. Proposed Work
4.1. Components Used
4.2. Framework Design and Implementation
4.3. Working Principle
Algorithm 1 Main Function of the Proposed Work |
Require: Ensure: 1: For Active
|
Algorithm 2 Body of the Procedure Active Nodes |
Require: Received via Ensure: Sent to
|
5. Simulation and Results
6. Conclusions and Future Scope
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Class Labels | Number of Samples | ||||
---|---|---|---|---|---|
Training Samples | Test Samples | Validation Samples | Total Samples | Resolution | |
Malignant (Considered Binary Value: 1) | 640 | 91 | 183 | 914 | 320 × 240 |
Benign (Considered Binary Value: 0) | 609 | 87 | 174 | 870 | 320 × 240 |
TL Approaches | Base Layer | Depth | Optimizer Used | Learning Rate | Epochs | Mini Batch Size | AF at Input Layers | AF at Hidden Layers | AF at Output Layers |
---|---|---|---|---|---|---|---|---|---|
ResNet50 | Without FC | 50 | Adam | 0.000001 | 50 | 24 | ReLU | ReLU | Softmax |
InceptionV3 | Without FC | 48 | Adam | 0.000001 | 50 | 24 | ReLU | ReLU | Softmax |
AlexNet | Without FC | 8 | Adam | 0.000001 | 50 | 24 | ReLU | ReLU | Softmax |
VGG16 | Without FC | 16 | Adam | 0.000001 | 50 | 24 | ReLU | ReLU | Sigmoid |
VGG19 | Without FC | 19 | Adam | 0.000001 | 50 | 24 | ReLU | ReLU | Sigmoid |
DTL Methods | Performance Measures (in %) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Acc | MCR | Pre | Sen | Spc | F1S | FPR | FNR | NPV | FDR | MCC | TSc | |
DTL-I | 91.47 | 8.53 | 96.03 | 94.42 | 65.18 | 95.22 | 5.58 | 34.82 | 56.71 | 3.97 | 56.06 | 90.87 |
DLT-II | 94.46 | 5.54 | 97.73 | 96.34 | 65.82 | 97.03 | 3.66 | 34.18 | 54.12 | 2.27 | 56.77 | 94.23 |
DTL-III | 88.63 | 11.37 | 94.53 | 92.36 | 62.36 | 93.43 | 7.64 | 37.64 | 53.66 | 5.47 | 51.35 | 87.68 |
DTL-IV | 97.99 | 2.01 | 99.51 | 98.43 | 80.08 | 98.97 | 1.57 | 19.92 | 55.14 | 0.49 | 65.51 | 97.95 |
DTL-V | 96.46 | 3.54 | 98.78 | 97.51 | 73.12 | 98.14 | 2.49 | 26.88 | 56.91 | 1.22 | 62.72 | 96.35 |
Configurations | Network Parameters | |||||
---|---|---|---|---|---|---|
Latency (in ms) | Arbitration Time (in ms) | Processing Time (in ms) | Jitter (in ms) | Network Utilization (in Secs) | Energy Consumption (in Watt) | |
SETUP-1 | 31.7 | 156.7 | 2435.2 | 6.25 | 9.3 | 3.49 |
SETUP-2 | 42.4 | 1046.5 | 3082.4 | 3.75 | 12.1 | 4.28 |
SETUP-3 | 36.5 | 1228.7 | 2897.5 | 4.50 | 14.8 | 5.26 |
SETUP-4 | 34.8 | 1847.5 | 3443.4 | 5.75 | 17.4 | 6.11 |
SETUP-5 | 41.3 | 2223.4 | 3273.6 | 8.50 | 18.7 | 6.83 |
SETUP-6 | 2318.9 | 142.3 | 1228.6 | 82.25 | 22.7 | 22.23 |
Work | Methodologies | Dataset Used | Performance Measures (in %) | ||||
---|---|---|---|---|---|---|---|
Acc | Pre | Sen | Spe | F1S | |||
[15] | CNN | Breast Histopathology Images (BHIs) Dataset | 88.46 | 85.46 | 95.17 | 82.64 | 79.77 |
[16] | Fuzzy c-means clustering algorithm | Medical CT scans | 94.6 | - | - | - | - |
[17] | CNN | Dataset from TCIA | - | - | - | - | - |
[18] | CNN | Dataset from TCIA | - | - | - | - | 94.2 |
[19] | CNN, SM, and RF | Dataset from Kaggle | 99.67 | - | - | - | - |
[20] | CNN and Deep CNN | Dataset from Kaggle | 84 | 74 | 71 | - | 70 |
[21] | CNN and TL | Dataset from TCIA | 97.0 | 96.0 | 89.0 | 94.0 | 98.0 |
[22] | CNN, SVM, DT, and NB | Dataset from Kaggle | 98.0 | - | 88.5 | - | - |
[23] | OMLTS-DLCN | Mini-MIAS and DDSM dataset | 98.50 | - | 98.46 | 99.08 | 98.91 |
[24] | CNN and TL | Breast Ultrasound Images (BUSIs) Dataset | 99.10 | 99.10 | 99.06 | - | 99.08 |
[25] | SVM, KNN, RF, NB, and AlexNet | MIAS dataset | 97.5 | 94.5 | 94.5 | 96.5 | 94.5 |
[26] | DCNN | Images from WCH, CMGH, and PHDY Hospitals | 87.0 | - | 86.0 | 88.0 | - |
[27] | VGG-16, VGG-19, and SqueezeNet | Benchmark Breast Ultrasound Dataset | 97.09 | 87.90 | 84.95 | 90.20 | - |
Proposed Work | CNN, VGG16, VGG19, ResNet50, AlexNet, and InceptionV3 | DDSM from TCIA | 97.97 | 99.51 | 98.43 | 80.08 | 98.97 |
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Pati, A.; Parhi, M.; Pattanayak, B.K.; Singh, D.; Singh, V.; Kadry, S.; Nam, Y.; Kang, B.-G. Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing. Diagnostics 2023, 13, 2191. https://doi.org/10.3390/diagnostics13132191
Pati A, Parhi M, Pattanayak BK, Singh D, Singh V, Kadry S, Nam Y, Kang B-G. Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing. Diagnostics. 2023; 13(13):2191. https://doi.org/10.3390/diagnostics13132191
Chicago/Turabian StylePati, Abhilash, Manoranjan Parhi, Binod Kumar Pattanayak, Debabrata Singh, Vijendra Singh, Seifedine Kadry, Yunyoung Nam, and Byeong-Gwon Kang. 2023. "Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing" Diagnostics 13, no. 13: 2191. https://doi.org/10.3390/diagnostics13132191
APA StylePati, A., Parhi, M., Pattanayak, B. K., Singh, D., Singh, V., Kadry, S., Nam, Y., & Kang, B.-G. (2023). Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing. Diagnostics, 13(13), 2191. https://doi.org/10.3390/diagnostics13132191