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Case Reports
. 2023 Feb 6:2023:5803661.
doi: 10.1155/2023/5803661. eCollection 2023.

A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

Parmod Kumar  1 , D Suganthi  2 , K Valarmathi  3 , Mahendra Pratap Swain  4 , Piyush Vashistha  5 , Dharam Buddhi  6 , Emmanuel Sey  7
Affiliations
Case Reports

A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models

Parmod Kumar et al. Biomed Res Int. .

Retraction in

Abstract

Cancer is one of the vital diseases which lead to the uncontrollable growth of the cell, and it affects the body tissue. A type of cancer that affects the children below five years and adults in a rare case is called retinoblastoma. It affects the retina in the eye and the surrounding region of eye like the eyelid, and sometimes, it leads to vision loss if it is not diagnosed at the early stage. MRI and CT are widely used scanning procedures to identify the cancerous region in the eye. Current screening methods for cancer region identification needs the clinicians' support to spot the affected regions. Modern healthcare systems develop an easy way to diagnose the disease. Discriminative architectures in deep learning can be viewed as supervised deep learning algorithms which use classification/regression techniques to predict the output. A convolutional neural network (CNN) is a part of the discriminative architecture which helps to process both image and text data. This work suggests the CNN-based classifier which classifies the tumor and nontumor regions in retinoblastoma. The tumor-like region (TLR) in retinoblastoma is identified using the automated thresholding method. After that, ResNet and AlexNet algorithms are used to classify the cancerous region along with classifiers. In addition, the comparison of discriminative algorithm along with its variants is experimented to produce the better image analysis method without the intervention of clinicians. The experimental study reveals that ResNet50 and AlexNet yield better results compared to other learning modules.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Fundus images of patients with and without RB.
Figure 2
Figure 2
Histogram representation for RB image.
Figure 3
Figure 3
RB image with TLR.
Figure 4
Figure 4
Architecture of the proposed methodology.
Figure 5
Figure 5
Different stages of retinoblastoma.
Figure 6
Figure 6
Confusion matrix for CNN models.
Figure 7
Figure 7
Classified images with and without RB.
Algorithm 1
Algorithm 1
See this image and copyright information in PMC

References

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    1. Anand C., Durai D., Jebaseeli J., Alelyani S., Mubharakali A. Early prediction and diagnosis of retinoblastoma using deep learning techniques. pp. 1–17. https://arxiv.org/abs/2103.07622 .
    1. Henning R., Rivas-Perea P., Shaw B., Hamerly G. A convolutional neural network approach for classifying leukocoria. 2014 Southwest Symposium on Image Analysis and Interpretation; 2014; San Diego, CA, USA. pp. 9–12. - DOI

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