A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Gupta, Koyel Datta; Sharma, Deepak Kumar; Ahmed, Shakib; Gupta, Harsh; Gupta, Deepak; Hsu, Ching‐Hsien

doi:10.1007/s11063-021-10555-1

Cited by 28 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(a) Utilizing various CNN algorithms for feature extraction from medical images [69], and (b) A hybrid approach that merges using a mathematical formula [70].…”

Section: Ensemble Methodsmentioning

confidence: 99%

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Agughasi

Murali

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Automatic identification of salient features in large medical datasets, particularly in chest X-ray (CXR) images, is a crucial research area. Accurately detecting critical findings such as emphysema, pneumothorax, and chronic bronchitis can aid radiologists in prioritizing time-sensitive cases and screening for abnormalities. However, traditional deep neural network approaches often require bounding box annotations, which can be time-consuming and challenging to obtain. This study proposes an explainable ensemble learning approach, CX-Net, for lung segmentation and diagnosing lung disorders using CXR images.We compare four state-of-the-art convolutional neural network (CNN) models, including FPN, U-Net, LinkNet, and a customized U-Net model with ImageNet feature extraction, data augmentation, and dropout regularizations. All models are trained on the Montgomery and VinDR-CXR datasets with and without segmented ground-truth masks. To achieve model explainability, we integrate SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) techniques, which enable a better understanding of the decision-making process and provide visual explanations of critical regions within the CXR images. By employing ensembling, our outlier-resistant CX-Net achieves superior performance in lung segmentation, with Jaccard overlap similarity (JS) of 0.992, Dice coefficients (DC) of 0.994, precision (PPV) of 0.993, recall of 0.980, and accuracy of 0.976. The proposed approach demonstrates strong generalization capabilities on the VinDr-CXR dataset and is the first study to use these datasets for semantic lung segmentation with semi-supervised localization. In conclusion, this paper presents an explainable ensemble learning approach for lung segmentation and diagnosing lung disorders using CXR images. Extensive experimental results show that our method efficiently and accurately extracts regions of interest in CXR images from publicly available datasets, indicating its potential for integration into clinical decision support systems (CDSS). Furthermore, incorporating SHAP and Grad-CAM techniques further enhances the interpretability and trustworthiness of the AI-driven diagnostic system.

show abstract

“…(a) Utilizing various CNN algorithms for feature extraction from medical images [69], and (b) A hybrid approach that merges using a mathematical formula [70].…”

Section: Ensemble Methodsmentioning

confidence: 99%

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Agughasi

Murali

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Model training is resource-intensive and timeconsuming, underscoring the importance of well-trained models to avoid inaccurate predictions with low probabilities. The integration of CNN principles into breast cancer detection elevates diagnostic accuracy and introduces a sophisticated and adaptable approach to healthcare [25]. This approach facilitates efficient pattern recognition and predictions within the dynamic landscape of medical data, marking a substantial advancement in diagnosing and managing breast cancer in humans [26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

Nana Yaw Duodu,

Warish D. Patel,

Hakan Koyuncu

2024

ARASET

View full text Add to dashboard Cite

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

show abstract

“…The studies demonstrated that although CNNs with greater scale or depth were able to result outstanding classification performance, they often have a significant drawback in execution time ( 9 , 14 - 16 ). Several lightweight architectures were proposed to address the drawback, which at the same time, able to yield a noticeable high accuracy (Acc) at a low latency ( 17 - 20 ).…”

Section: Introductionmentioning

confidence: 99%

Non-annotated renal histopathological image analysis with deep ensemble learning

Koo,

Ke,

Hum

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients’ survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage. Methods In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using the pre-trained CNNs and lastly perform the deep ensemble learning to determine the final classification. The heterogeneous ensemble models consisted of CNN models from five deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet. These CNN models were fine-tuned and used as base learners, they exhibited different performances and had great diversity in histopathological image analysis. The CNN models with superior classification accuracy (Acc) were then selected to undergo ensemble learning for the final classification. The performance of the investigated ensemble approaches was evaluated against the state-of-the-art literature. Results The performance evaluation demonstrated the superiority of the proposed best performing ensembled model: five-CNN based weighted averaging model, with an Acc (99%), specificity (Sp) (98%), F1-score (F1) (99%) and area under the receiver operating characteristic (ROC) curve (98%) but slightly inferior recall (Re) (99%) compared to the literature. Conclusions The outstanding robustness of the developed ensemble model with a superiorly high-performance scores in the evaluated metrics suggested its reliability as a diagnosis system for assisting the pathologists in analyzing the renal histopathological tissues. It is expected that the proposed ensemble deep CNN models can greatly improve the early detection of renal cancer by making the diagnosis process more efficient, and less misdetection and misdiagnosis; subsequently, leading to higher patients’ survival rate.

show abstract

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Cited by 28 publications

References 34 publications

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

Non-annotated renal histopathological image analysis with deep ensemble learning

Contact Info

Product

Resources

About