Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification

• URL: http://arxiv.org/abs/2405.04610v2
• Date: Tue, 14 May 2024 16:02:11 GMT
• Title: Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification
• Authors: Mukaffi Bin Moin, Fatema Tuj Johora Faria, Swarnajit Saha, Busra Kamal Rafa, Mohammad Shafiul Alam,
• Abstract summary: Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks. Histopathology remains the gold standard, although time-consuming and vulnerable to inter-observer mistakes. Recent advances in deep learning have generated interest in its application to medical imaging analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks. However, diagnosis, which is mostly dependent on histopathologists' competence, presents difficulties and hazards when expertise is insufficient. While diagnostic methods like imaging and blood markers contribute to early detection, histopathology remains the gold standard, although time-consuming and vulnerable to inter-observer mistakes. Limited access to high-end technology further limits patients' ability to receive immediate medical care and diagnosis. Recent advances in deep learning have generated interest in its application to medical imaging analysis, specifically the use of histopathological images to diagnose lung and colon cancer. The goal of this investigation is to use and adapt existing pre-trained CNN-based models, such as Xception, DenseNet201, ResNet101, InceptionV3, DenseNet121, DenseNet169, ResNet152, and InceptionResNetV2, to enhance classification through better augmentation strategies. The results show tremendous progress, with all eight models reaching impressive accuracy ranging from 97% to 99%. Furthermore, attention visualization techniques such as GradCAM, GradCAM++, ScoreCAM, Faster Score-CAM, and LayerCAM, as well as Vanilla Saliency and SmoothGrad, are used to provide insights into the models' classification decisions, thereby improving interpretability and understanding of malignant and benign image classification.

Related papers

• From Images to Insights: Transforming Brain Cancer Diagnosis with Explainable AI [1.939732664561742]
  This study conveys the Bangladesh Brain Cancer MRI dataset, containing 6,056 MRI images organized into three categories: Brain Tumor, Brain Glioma, and Brain Menin. DenseNet169 achieved exceptional results, with accuracy, precision, recall, and F1-Score all reaching 0.9983.
  arXiv  Detail & Related papers  (2025-01-09T18:35:43Z)
• MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification [0.0]
  Lung and colon cancers are predominant contributors to cancer mortality. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification. We proposed a novel approach based on a modified residual attention network architecture. Our proposed model achieved an exceptional accuracy of 99.30%, 96.63%, and 97.56% for two, three, and five classes, respectively.
  arXiv  Detail & Related papers  (2024-12-23T16:31:45Z)
• A Knowledge-enhanced Pathology Vision-language Foundation Model for Cancer Diagnosis [58.85247337449624]
  We propose a knowledge-enhanced vision-language pre-training approach that integrates disease knowledge into the alignment within hierarchical semantic groups. KEEP achieves state-of-the-art performance in zero-shot cancer diagnostic tasks.
  arXiv  Detail & Related papers  (2024-12-17T17:45:21Z)
• Fast-staged CNN Model for Accurate pulmonary diseases and Lung cancer detection [0.0]
  This research evaluates a deep learning model designed to detect lung cancer, specifically pulmonary nodules, along with eight other lung pathologies, using chest radiographs. A two-stage classification system, utilizing ensemble methods and transfer learning, is employed to first triage images into Normal or Abnormal. The model achieves notable results in classification, with a top-performing accuracy of 77%, a sensitivity of 0.713, a specificity of 0.776 during external validation, and an AUC score of 0.888.
  arXiv  Detail & Related papers  (2024-12-16T11:47:07Z)
• Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images [0.0]
  This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. We propose a novel hybrid model that combines convolutional neural networks (CNN) and vision transformers (ViT) to improve diagnostic accuracy for OS. The model achieved an accuracy of 99.08%, precision of 99.10%, recall of 99.28%, and an F1-score of 99.23%.
  arXiv  Detail & Related papers  (2024-10-29T13:54:08Z)
• Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis [11.148919818020495]
  Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via Whole Slide Image (WSI) diagnosis is critical to prevent this Cancer progression and improve survival rate. But pathologist's single test suffers inevitable false negative due to the immense number of cells that need to be reviewed within a WSI.
  arXiv  Detail & Related papers  (2024-07-28T15:29:07Z)
• WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma [51.50991881342181]
  This challenge includes 10,091 patch-level annotations and over 130 million labeled pixels. First place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919)
  arXiv  Detail & Related papers  (2022-04-13T15:27:05Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• 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)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)

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.