i-Net: a deep CNN model for white blood cancer segmentation and classification

doi:10.19101/ijatee.2021.875564

Cited by 14 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result of adding more data, the models were more accurate and had less overfitting. Similarly, the researches [23]- [26] investigated the use of ensemble approaches for semantic segmentation using pre-trained U-Net and VGG19 models. The success of ensemble approaches and CNN-based detectors in achieving high accuracy and precision further attests to the potent capabilities of DL in transforming DR diagnostic processes.…”

Section: Deep Learning Approaches For Dr Diagnosismentioning

confidence: 99%

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

Bhoopal,

Rao,

Krishnappa

2024

IJEECS

View full text Add to dashboard Cite

Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.

show abstract

Section: Deep Learning Approaches For Dr Diagnosismentioning

confidence: 99%

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

Bhoopal,

Rao,

Krishnappa

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…Given that all images were in DICOM format, a Python script was developed to convert DICOM to the corresponding photographic network group (PNG) format, as shown in table 3. To expedite training, all images were downscaled to 224 × 224, an approach that proved successful in segmenting microscopic white blood cells (WBCs) [68] for clinical diagnosis after normalization. This process ensures pixel intensity lies between 0 and 1, where 0 represents an entirely dark image, and 1 denotes an utterly bright image.…”

Section: Image Pre-processing and Enhancementmentioning

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

“…To use the abundance of DICOM images, we coded a Python script to transform DICOM files into their matching PNG files automatically. To facilitate quicker training, we reduced the images' resolution to 224 × 224, which proved effective when segmenting microscopic white blood cells (WBC) [31] for clinical diagnosis following normalization. It ensures the intensity of each pixel is between 0 and 1; 0 is completely black and 1 is entirely white.…”

Section: Pre-processing Of Imagesmentioning

confidence: 99%

Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation

Agughasi,

Srinivasiah

2023

AET

View full text Add to dashboard Cite

Supervised classifiers require a lot of data with accurate labels to learn to recognize chest X-ray images (CXR). However, manually labeling an extensive collection of CXR images is time-consuming and costly. To address this issue, a method for the semi-supervised labelling of extensive collections of CXR images is proposed leveraging unsupervised clustering with minimum expert knowledge to generate ground truth images. The proposed methodology entails: using unsupervised clustering techniques such as K-Means and Self-Organizing Maps. Second, the images are fed to five different feature vectors to utilize the potential differences between features to their full advantage. Third, each data point gets the label of the cluster’s center to which it belongs. Finally, a majority vote is used to decide the ground truth image. The number of clusters created by the method chosen strictly limits the amount of human involvement. To evaluate the effectiveness of the proposed method, experiments were conducted on two publicly available CXR datasets, namely VinDR-CXR and Montgomery datasets. The experiments showed that, for a KNN classifier, manually labeling only 1% (VinDr-CXR), or 10% (Montgomery) of the training data, gives a similar performance as labeling the whole dataset. The proposed methodology efficiently generates ground-truth images from publicly available CXR datasets. To our knowledge, this is the first study to use the VinDr-CXR and Montgomery datasets for ground truth image generation. Extensive experimental analysis using machine learning and statistical techniques shows that the proposed methodology efficiently generates ground truth images from CXR datasets.

show abstract

i-Net: a deep CNN model for white blood cancer segmentation and classification

Cited by 14 publications

References 39 publications

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

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

Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation

Contact Info

Product

Resources

About