Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images DOI Creative Commons

Veysel Yusuf Cambay,

Prabal Datta Barua, Abdul Hafeez‐Baig

и другие.

Sensors, Год журнала: 2024, Номер 24(23), С. 7710 - 7710

Опубликована: Дек. 2, 2024

This work aims to develop a novel convolutional neural network (CNN) named ResNet50* detect various gastrointestinal diseases using new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this is the development ResNet50*, variant ResNet model, featuring convolution-based residual blocks and pooling-based attention mechanism similar PoolFormer. Using image dataset was trained, an explainable (DFE) developed. DFE comprises four primary stages: (i) extraction, (ii) iterative selection, (iii) classification shallow classifiers, (iv) information fusion. self-organizing, producing 14 different outcomes (8 classifier-specific 6 voted) selecting most effective result as final decision. During heatmaps are identified gradient-weighted class activation mapping (Grad-CAM) features derived from these regions via global average pooling layer pretrained ResNet50*. Four selectors employed in selection stage obtain distinct vectors. classifiers k-nearest neighbors (kNN) support vector machine (SVM) used produce specific outcomes. Iterative majority voting voted top determined by greedy algorithm based on accuracy. presented trained augmented version Kvasir dataset, its performance tested Kvasir, 2, wireless capsule (WCE) curated colon disease datasets. Our proposed demonstrated accuracy more than 92% for all three datasets remarkable 99.13% WCE dataset. These findings affirm superior ability confirm generalizability developed architecture, showing consistent across

Язык: Английский

Transfer Learning in Endoscopic Imaging: A Machine Vision Approach to GIT Disease Identification DOI

Jayavrinda Vrindavanam,

Pradeep Kumar,

Gaurav Kamath

и другие.

Опубликована: Май 22, 2024

Язык: Английский

Процитировано

1
Gastro Intestinal Disease Classification Using Hierarchical Spatio Pyramid TranfoNet With PitTree Fusion and Efficient-CondConv SwishNet DOI Creative Commons

V. Sharmila,

S. Geetha

IEEE Access, Год журнала: 2024, Номер 12, С. 113972 - 113987

Опубликована: Янв. 1, 2024

Early detection of Gastrointestinal (GI) tract diseases is essential for effective healthcare management, treatment, and prevention, ultimately lowering morbidity mortality rates worldwide. Current classification models lack spatial feature arrangement consideration, diminishing discriminative power leading to misdiagnosis esophagitis ulcerative colitis due overlapping visual characteristics with other GI diseases. Hence, a novel Hierarchical Spatio Pyramid TranfoNet featuring Spatial Transformer Network (STN) pyramid pooling introduced, which enhances in distinguishing between disease characteristics. Enhancing Dyed Lifted Polyps (DLP) Resection Margins (DRM) endoscopy images critical precise gastrointestinal diagnosis, tackling challenges posed by complexity inter-class confounders. PitTree Fusion Algorithm, combining Minimum Spanning Tree (MST) analysis Kudo's pit pattern introduced accurately locate differentiate normal tissue from dyed regions like DLPs DRMs images. Then, Efficient-CondConv SwishNet enhance extracting informative features endoscopic images, utilizing EfficientNet-CondConv Swish activation. After classification, heatmaps highlighting influential are produced via gradient-weighted class activation mapping, or Grad-CAM, provides information about decisions. The results show that the suggested model outperforms current showing increased accuracy, precision, recall, sensitivity, specificity, F1 score, reduced loss rate.

Язык: Английский

Процитировано

1
Comparative exploration of deep convolutional neural networks using real-time endoscopy images DOI
Subhashree Mohapatra,

Pukhraj Singh Jeji,

Girish Kumar Pati

и другие.

Biomedical Technology, Год журнала: 2024, Номер 8, С. 1 - 16

Опубликована: Сен. 20, 2024

Язык: Английский

Процитировано

1
Investigation and evaluation of cross-term reduction in masked Wigner-Ville distributions using S-transforms DOI Creative Commons
Nattapol Aunsri, Prasara Jakkaew, Chanin Kuptametee

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(11), С. e0310721 - e0310721

Опубликована: Ноя. 6, 2024

Non-linear and non-stationary signals are analyzed processed in the time-frequency (TF) domain due to interpretation simplicity. Wigner-Ville distribution (WVD) delivers a very sharp resolution of TF domain. However, cross-terms occur between true frequency modes their bilinear nature. Masked WVD reduces by multiplying representation (TFR) obtained from with TFR same signal another method, while S-transform (ST) is linear analysis method that combines advantages short-time Fourier transform (STFT) wavelet (WT). This paper investigated masking both original modified STs compare cross-term reduction results. Moreover, additional parameters integrated into ST deliver better and, consequently, more satisfactory reduction. these must be carefully optimized expert users respective application fields.

Язык: Английский

Процитировано

1
Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images DOI Creative Commons

Veysel Yusuf Cambay,

Prabal Datta Barua, Abdul Hafeez‐Baig

и другие.

Sensors, Год журнала: 2024, Номер 24(23), С. 7710 - 7710

Опубликована: Дек. 2, 2024

This work aims to develop a novel convolutional neural network (CNN) named ResNet50* detect various gastrointestinal diseases using new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this is the development ResNet50*, variant ResNet model, featuring convolution-based residual blocks and pooling-based attention mechanism similar PoolFormer. Using image dataset was trained, an explainable (DFE) developed. DFE comprises four primary stages: (i) extraction, (ii) iterative selection, (iii) classification shallow classifiers, (iv) information fusion. self-organizing, producing 14 different outcomes (8 classifier-specific 6 voted) selecting most effective result as final decision. During heatmaps are identified gradient-weighted class activation mapping (Grad-CAM) features derived from these regions via global average pooling layer pretrained ResNet50*. Four selectors employed in selection stage obtain distinct vectors. classifiers k-nearest neighbors (kNN) support vector machine (SVM) used produce specific outcomes. Iterative majority voting voted top determined by greedy algorithm based on accuracy. presented trained augmented version Kvasir dataset, its performance tested Kvasir, 2, wireless capsule (WCE) curated colon disease datasets. Our proposed demonstrated accuracy more than 92% for all three datasets remarkable 99.13% WCE dataset. These findings affirm superior ability confirm generalizability developed architecture, showing consistent across

Язык: Английский

Процитировано

1