Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique

Jebaseeli, T. Jemima; David, D. Jasmine

doi:10.1016/b978-0-323-95415-0.00005-x

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…This suggests that the transfer learning approach using InceptionV3 has been effective in improving the accuracy of the model. Moreover, in the study by Jebaseeli et al [55], the linear predictive decision-based median filter and 2.75D CNN achieved an accuracy of 98.84%, sensitivity of 97.96%, and specificity of 98.32%. While their model achieved high accuracy and sensitivity, it performed slightly lower in terms of specificity compared to our model.…”

Section: Discussionmentioning

confidence: 94%

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Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

et al. 2023

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Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a “black box” that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model’s predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model’s predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.

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Section: Discussionmentioning

confidence: 94%

“…The performance of ResNet and AlexNet was compared, and ResNet50 was found to yield the highest classification performance of 93.16%. In another paper, by Jebaseeli et al [55], the focus is on the detection of retinoblastoma through image analysis.…”

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confidence: 99%

Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

et al. 2023

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Semi-supervised segmentation of retinoblastoma tumors in fundus images

Rahdar,

Ahmadi,

Naseripour

et al. 2023

Sci Rep

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Retinoblastoma is a rare form of cancer that predominantly affects young children as the primary intraocular malignancy. Studies conducted in developed and some developing countries have revealed that early detection can successfully cure over 90% of children with retinoblastoma. An unusual white reflection in the pupil is the most common presenting symptom. Depending on the tumor size, shape, and location, medical experts may opt for different approaches and treatments, with the results varying significantly due to the high reliance on prior knowledge and experience. This study aims to present a model based on semi-supervised machine learning that will yield segmentation results comparable to those achieved by medical experts. First, the Gaussian mixture model is utilized to detect abnormalities in approximately 4200 fundus images. Due to the high computational cost of this process, the results of this approach are then used to train a cost-effective model for the same purpose. The proposed model demonstrated promising results in extracting highly detailed boundaries in fundus images. Using the Sørensen–Dice coefficient as the comparison metric for segmentation tasks, an average accuracy of 93% on evaluation data was achieved.

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Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort

Vempuluru,

Viriyala,

Ayyagari

et al. 2024

Cancers

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Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget’s Statistical Policy Directive No.15 and 5 (2%) had no reported race. Of the 2473 images in 210 eyes, 427 had no tumor, and 2046 had iRB. After training the AI model based on race, the sensitivity and specificity for detection of RB in 2473 images were 93% and 96%, respectively. The sensitivity and specificity of the AI model were 74% and 100% for group A; 88% and 96% for group B; 88% and 100% for group C; 73% and 98% for group D, and 100% and 92% for group E, respectively. Conclusions: The AI models built on a single race do not work well for other races. When retrained for different races, our model exhibited high sensitivity and specificity in detecting RB and classifying RB.

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Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique

Cited by 3 publications

References 29 publications

Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

Semi-supervised segmentation of retinoblastoma tumors in fundus images

Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort

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