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The Retinoblastoma Gene

The Retinoblastoma Gene

Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
The first-ever tumor suppressor gene called Rb was identified in retinoblastoma - a rare eye tumor in children. In inherited forms of the disease, a child inherits one defective copy of the Rb gene, which predisposes them to retinoblastoma. However,...

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Updated: Jul 8, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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.

Peerj. Computer Science

|December 11, 2023

Summary

This summary is machine-generated.

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.

Keywords:

Deep learning Ophthalmology Retinal tumor Retinoblastoma Segmentation

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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.