Artificial Intelligence in Healthcare
An attention based optimized network for the classification of skin lesions
This paper proposes a novel and optimal method for classifying different types of skin lesions by combining deep learning with optimization techniques. The HHO-Reg-SA-Net model, leveraging RegNetY032 as a backbone with a modified classification head and an integrated Soft Attention Block, effectively discerns salient lesion features while disregarding artifacts. Harris-Hawks Optimization (HHO) is used for hyperparameter tuning. Experimental results on the HAM10000 dataset demonstrate a superior classification accuracy of 99.27%, offering promise for automated dermatological diagnosis.
Executive Summary: Unlocking Predictive Diagnostics
Delayed diagnosis of skin lesions, particularly in underserved regions, poses significant healthcare challenges. Our HHO-Reg-SA-Net model provides a robust, highly accurate AI solution that can significantly improve early detection and patient outcomes. By automating classification with advanced deep learning and optimization, enterprises in healthcare can enhance diagnostic efficiency, reduce costs associated with late-stage treatments, and expand access to critical dermatological expertise.
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Classification Accuracy
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Deep Analysis & Enterprise Applications
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The HHO-Reg-SA-Net model integrates a RegNetY032 backbone for feature extraction, enhanced with a Soft Attention Block to focus on crucial lesion features. This refined architecture then undergoes hyperparameter optimization using the Harris-Hawks Optimization algorithm, ensuring optimal performance and rapid convergence. This systematic approach tackles class imbalance and noise effectively.
RegNetY032, a member of the RegNet family, is chosen for its efficiency and scalability. Unlike fixed architectures, RegNet explores a wide design space to find optimal network configurations. The incorporation of a modified classification head and the Soft Attention Block further refines its ability to learn complex, nuanced relationships in dermoscopic images, which is critical for accurate skin lesion classification.
Harris-Hawks Optimization (HHO) is a swarm-based metaheuristic algorithm inspired by the cooperative hunting behavior of Harris hawks. In this work, HHO is employed to fine-tune the hyperparameters of the deep learning model, leading to significant improvements in classification performance, faster convergence, and reduced training time. This optimization step is crucial for achieving the reported high accuracy.
99.27%
Superior Classification Accuracy Achieved on HAM10000
HHO-Reg-SA-Net Enterprise Process Flow
Input Raw Dermoscopic Images
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Image Preprocessing & Augmentation
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RegNetY032 Feature Extraction (Backbone)
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Soft Attention Block (Salient Feature Prioritization)
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Harris-Hawks Hyperparameter Optimization
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Trained HHO-Reg-SA-Net Model
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Automated Skin Lesion Classification
| Model | Accuracy | Precision | Recall | F1-Score | Specificity |
|---|---|---|---|---|---|
| Bilinear CNN | 93.21 | 92.92 | 93.00 | 93.21 | - |
| InceptionV3 + DarkNet53 + MFO | 95.80 | 95.11 | 91.75 | - | - |
| Ensemble Learning | 88.00 | 87.00 | 94.00 | 89.00 | - |
| SBXception Network | 96.97 | 85.34 | 95.43 | - | - |
| MHCL-PO | 92.20 | 90.20 | 84.72 | 86.41 | - |
| Fully Transformer Network | 92.70 | 85.70 | 62.10 | - | 93.60 |
| Reg-SA-Net [ours] | 98.99 | 98.71 | 98.06 | 98.09 | 98.17 |
| HHO-Reg-SA-Net [ours] | 99.27 | 99.24 | 99.07 | 99.14 | 99.09 |
Enterprise Challenge: Bridging the Diagnostic Gap in Dermatology
The scarcity of dermatological resources, especially in rural areas, leads to delayed skin lesion diagnoses, exacerbating morbidities and melanoma mortality. Manual diagnosis is prone to variability and requires extensive expert knowledge, which is globally unevenly distributed. An automated, highly accurate system like HHO-Reg-SA-Net addresses these critical challenges by providing a consistent, unbiased, and accessible diagnostic tool. This empowers healthcare providers to deliver timely interventions, leading to improved patient outcomes and reduced healthcare costs associated with advanced disease stages. Early detection is paramount, and AI-driven diagnostics offer a scalable solution for enterprises seeking to revolutionize patient care.
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Initial Assessment & Strategy
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Data Integration & Preprocessing
Securely integrate existing dermoscopic image datasets, perform comprehensive data augmentation, and ensure privacy compliance. Custom preprocessing pipelines are developed.
Model Customization & Optimization
Fine-tune the HHO-Reg-SA-Net model to your specific data, leveraging Harris-Hawks Optimization for peak performance and faster training, ensuring robust and accurate predictions.
Deployment & Continuous Monitoring
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