Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Abstract

Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.

Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases

“…This section presents the performance of the pre-trained CNN models AlexNet, Res-Net-50, GoogLeNet, and ResNet-18 used to evaluate the microscopic blood sample dataset for the early detection of WBCs [41,42]. All dataset images were resized to fit the model.…”

“…Equations ( 13)-( 17) describe the statistical measures of accuracy, precision, sensitivity, specificity, and AUC used in this work to assess the performance of the systems. All of the proposed methods provide a confusion matrix that includes all successfully identified test images (TP and TN) as well as poorly classified images (FP and FN) [40][41][42][43][44]. Thus, using the information provided by the confusion matrix, the performance of the systems is calculated with the equations below: where the true positive (TP) is the unhealthy WBCs that have been correctly diagnosed, true negative (TN) is the healthy WBCs from correctly diagnosed normal patients, false negative (FN) is the blasted WBCs diagnosed as normal, and false positive (FP) is a normal WBC count diagnosed as blasted WBCs.…”

A Comprehensive Study: Intracranial Aneurysm Detection via Vgg16-Densenet Hybrid Deep Learning on Dsa Images

“…The data was trained utilizing a pre-existing Deep Learning model DenseNet-121 [8]. To initiate model training, the pretrained DenseNet-121 model was loaded with pre-trained weights from the ImageNet dataset.…”

“…However, accurate and timely detection of intracranial aneurysms is a unique challenge that can be solved by combining the advantages of these two methods. This study leverages the power of deep learning by providing composite models that leverage diversity inference provided by VGG16 and DenseNet [8].…”

Emerging Trends and Advances in the Diagnosis of Gastrointestinal Diseases

“…Different histogram-oriented gradients (HOG) features were combined, which were extracted from LBP (Local Binary Patterns), and were then passed to extreme machines for learning and detecting stomach diseases [38]. In [39] utilized a Neural Network to meld includes and got 95.5% accuracy and 92.8% F1 score, which worked on the presentation of diagnosing thyroid knobs. In another research [40], the author combined carefully assembled multiple handcrafted features like LBP, DWT, and GLCM.…”