COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images

Ikechukwu, Agughasi Victor; S, Murali; B, Honnaraju

doi:10.1109/indiscon58499.2023.10270604

Cited by 8 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reinforcement learning (RL) refers to a class of algorithms used to improve the efficiency of Markov property-based sequential decision-making [15], [16]. These algorithms operate by learning to make decisions through trial and error, essentially learning a strategy or policy that maps states of the world to the actions that should be taken in those states.…”

Section: Related Work 21 Reinforcement Learningmentioning

confidence: 99%

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Bhimshetty,

Victor Ikechukwu

2024

IJEECS

View full text Add to dashboard Cite

<div align="center"><span>The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.</span></div>

show abstract

Section: Related Work 21 Reinforcement Learningmentioning

confidence: 99%

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Bhimshetty,

Victor Ikechukwu

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…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

“…Notably, generative adversarial network (GAN) by Goodfellow et al [34] has gained prominence for image enhancement and transformation tasks. Research has shown GANs, including models like CycleGAN [35] and pix2pix-HD [36], to be instrumental in semantic segmentation and highresolution image translations. Moreover, innovative text detection strategies have emerged, with works like [37], [38] leveraging region proposal networks (RPN) for texts with varied orientations.…”

Section: Introductionmentioning

confidence: 99%

CRNN model for text detection and classification from natural scenes

Prakash,

Yeliyur Hanumanthaiah,

Bannur Mayigowda

2024

IJ-AI

View full text Add to dashboard Cite

<span lang="EN-US">In the emerging field of computer vision, text recognition in natural settings remains a significant challenge due to variables like font, text size, and background complexity. This study introduces a method focusing on the automatic detection and classification of cursive text in multiple languages: English, Hindi, Tamil, and Kannada using a deep convolutional recurrent neural network (CRNN). The architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for effective spatial and temporal learning. We employed pre-trained CNN models like VGG-16 and ResNet-18 for feature extraction and evaluated their performance. The method outperformed existing techniques, achieving an accuracy of 95.0%, 96.3%, and 96.2% on ICDAR 2015, ICDAR 2017, and a custom dataset (PDT2023), respectively. The findings not only push the boundaries of text detection technology but also offer promising prospects for practical applications.</span>

show abstract

COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images

Cited by 8 publications

References 30 publications

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things

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

CRNN model for text detection and classification from natural scenes

Contact Info

Product

Resources

About