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
- Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction [1.48419209885019]
This work introduces a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval.
We evaluated the performance of the proposed model on two publicly available datasets of breast cancer histological images.
arXiv Detail & Related papers (2024-05-07T11:24:37Z) - Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example [40.3927727959038]
This paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images.
It enables the rapid and automatic classification of pathological images into benign and malignant groups.
It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer.
arXiv Detail & Related papers (2024-04-12T07:08:05Z) - Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
"Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions.
We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
arXiv Detail & Related papers (2023-07-30T16:08:45Z) - Breast Cancer Segmentation using Attention-based Convolutional Network
and Explainable AI [0.0]
Breast cancer (BC) remains a significant health threat, with no long-term cure currently available.
Early detection is crucial, yet mammography interpretation is hindered by high false positives and negatives.
This work presents an attention-based convolutional neural network for segmentation, providing increased speed and precision in BC detection and classification.
arXiv Detail & Related papers (2023-05-22T20:49:20Z) - 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) - A Review of Generative Adversarial Networks in Cancer Imaging: New
Applications, New Solutions [12.1951719081621]
Recent advancements in Generative Adrial Networks (GANs) in computer vision may provide a basis for enhanced capabilities in cancer detection and analysis.
We assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance.
We provide a critical appraisal of the existing literature of GANs applied to cancer imagery, together with suggestions on future research directions to address these challenges.
arXiv Detail & Related papers (2021-07-20T14:57:51Z) - Weighted multi-level deep learning analysis and framework for processing
breast cancer WSIs [0.10499611180329801]
We present a deep learning-based solution and framework for processing Whole Slide Images (WSI) based on a novel approach utilizing the advantages of image levels.
Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%.
arXiv Detail & Related papers (2021-06-28T13:38:11Z) - 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.