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Advancing retinoblastoma detection based on binary arithmetic optimization and integrated features
Nuha Alruwais1, Marwa Obayya2, Fuad Al-Mutiri3
1Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, Riyadh, Saudi Arabia.
Abstract:
Retinoblastoma, the most prevalent pediatric intraocular malignancy, can cause vision loss in children and adults worldwide. Adults may develop uveal melanoma. It is a hazardous tumor that can expand swiftly and destroy the eye and surrounding tissue. Thus, early retinoblastoma screening in children is essential. This work isolated retinal tumor cells, which is its main contribution. Tumors were also staged and subtyped. The methods let ophthalmologists discover and forecast retinoblastoma malignancy early. The approach may prevent blindness in infants and adults. Experts in ophthalmology now have more tools because of their disposal and the revolution in deep learning techniques. There are three stages to the suggested approach, and they are pre-processing, segmenting, and classification. The tumor is isolated and labeled on the base picture using various image processing techniques in this approach. Median filtering is initially used to smooth the pictures. The suggested method's unique selling point is the incorporation of fused features, which result from combining those produced using deep learning models (DL) such as EfficientNet and CNN with those obtained by more conventional handmade feature extraction methods. Feature selection (FS) is carried out to enhance the performance of the suggested system further. Here, we present BAOA-S and BAOA-V, two binary variations of the newly introduced Arithmetic Optimization Algorithm (AOA), to perform feature selection. The malignancy and the tumor cells are categorized once they have been segmented. The suggested optimization method enhances the algorithm's parameters, making it well-suited to multimodal pictures taken with varying illness configurations. The proposed system raises the methods' accuracy, sensitivity, and specificity to 100, 99, and 99 percent, respectively. The proposed method is the most effective option and a viable alternative to existing solutions in the market.
Insights
This study introduces a novel deep learning approach for early detection and classification of retinoblastoma, a common childhood eye cancer. The method accurately identifies and stages tumors, aiding ophthalmologists in preventing vision loss.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Retinoblastoma is the most common pediatric intraocular malignancy, potentially leading to vision loss.
- Early detection and accurate staging are crucial for effective treatment and preventing blindness.
- Existing diagnostic methods can be enhanced with advanced computational techniques.
Purpose of the Study:
- To develop and validate a novel deep learning-based system for the early detection, segmentation, and classification of retinoblastoma.
- To improve the accuracy and efficiency of retinoblastoma diagnosis using fused image features.
- To provide ophthalmologists with advanced tools for forecasting tumor malignancy and preventing vision loss in children and adults.
Main Methods:
- A three-stage approach involving image pre-processing, segmentation, and classification of retinal tumor cells.
- Utilizing median filtering for image smoothing and combining deep learning (EfficientNet, CNN) with traditional feature extraction methods.
- Implementing feature selection using binary variations of the Arithmetic Optimization Algorithm (BAOA-S and BAOA-V) for enhanced performance.
Main Results:
- The proposed system achieved high accuracy, sensitivity, and specificity rates of 100%, 99%, and 99%, respectively.
- Successfully isolated, staged, and subtyped retinal tumors, enabling early malignancy prediction.
- Demonstrated superior performance compared to existing market solutions for retinoblastoma diagnosis.
Conclusions:
- The developed deep learning framework offers a robust and effective solution for early retinoblastoma detection and classification.
- The fusion of deep learning and traditional features, coupled with advanced optimization algorithms, significantly enhances diagnostic capabilities.
- This approach holds the potential to revolutionize pediatric eye cancer screening and significantly reduce cases of blindness.
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