Explainable AI based Glaucoma Detection using Transfer Learning and LIME

• URL: http://arxiv.org/abs/2210.03332v1
• Date: Fri, 7 Oct 2022 05:36:33 GMT
• Title: Explainable AI based Glaucoma Detection using Transfer Learning and LIME
• Authors: Touhidul Islam Chayan, Anita Islam, Eftykhar Rahman, Md. Tanzim Reza, Tasnim Sakib Apon, MD. Golam Rabiul Alam
• Abstract summary: We propose a transfer learning model that is able to classify Glaucoma with 94.71% accuracy. We have utilized Local Interpretable Model-Agnostic Explanations(LIME) that introduces explainability in our system.
• Score: 0.3914676152740142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Glaucoma is the second driving reason for partial or complete blindness among all the visual deficiencies which mainly occurs because of excessive pressure in the eye due to anxiety or depression which damages the optic nerve and creates complications in vision. Traditional glaucoma screening is a time-consuming process that necessitates the medical professionals' constant attention, and even so time to time due to the time constrains and pressure they fail to classify correctly that leads to wrong treatment. Numerous efforts have been made to automate the entire glaucoma classification procedure however, these existing models in general have a black box characteristics that prevents users from understanding the key reasons behind the prediction and thus medical practitioners generally can not rely on these system. In this article after comparing with various pre-trained models, we propose a transfer learning model that is able to classify Glaucoma with 94.71\% accuracy. In addition, we have utilized Local Interpretable Model-Agnostic Explanations(LIME) that introduces explainability in our system. This improvement enables medical professionals obtain important and comprehensive information that aid them in making judgments. It also lessen the opacity and fragility of the traditional deep learning models.

Related papers

• AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review [0.09320657506524149]
  Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early. This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis.
  arXiv  Detail & Related papers  (2024-10-21T12:26:53Z)
• InceptionCaps: A Performant Glaucoma Classification Model for Data-scarce Environment [0.0]
  glaucoma is an irreversible ocular disease and is the second leading cause of visual disability worldwide. This work reviews existing state of the art models and proposes InceptionCaps, a novel capsule network (CapsNet) based deep learning model having pre-trained InceptionV3 as its convolution base, for automatic glaucoma classification. InceptionCaps achieved an accuracy of 0.956, specificity of 0.96, and AUC of 0.9556, which surpasses several state-of-the-art deep learning model performances on the RIM-ONE v2 dataset.
  arXiv  Detail & Related papers  (2023-11-24T11:58:11Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• A CNN-LSTM Combination Network for Cataract Detection using Eye Fundus Images [0.0]
  One of the leading causes of irreversible blindness in persons over the age of 50 is delayed cataract treatment. We developed a CNN-LSTM-based model architecture with the goal of creating a low-cost diagnostic system. The suggested architecture outperformed previous systems with a state-of-the-art 97.53% accuracy.
  arXiv  Detail & Related papers  (2022-10-28T12:35:15Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions [31.71252686825754]
  We propose OpticNet, a novel optical refraction network, loss function, and training scheme. We derive a precise light propagation eye model using single-ray raytracing. We show that our network is superior to systems trained with standard procedures.
  arXiv  Detail & Related papers  (2020-05-09T14:39:23Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.