Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases

Fati, Suliman Mohamed; Senan, Ebrahim Mohammed; Azar, Ahmad Taher

doi:10.3390/s22114079

Cited by 29 publications

(23 citation statements)

References 47 publications

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“…The model contains 49 convolutional layers with filters of different sizes to extract the features by wrapping the filter f ( t ) around the image x ( t ) as in Equation (4) [ 43 ]. The pooling layers receive huge amounts of neurons, which require complex operations that take a long time, and these layers work to reduce the high dimensions [ 44 ]. The model contains average pooling layers that calculate the average of the selected neurons and replace the selected neurons with their average value as in Equation (5).…”

Section: Methodsmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

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Section: Methodsmentioning

confidence: 99%

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

et al. 2023

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“…The max-pooling layers reduce the high dimensions by representing a group of pixels by their max value, as in Equation (2). In contrast, the average pooling layers reduce the high dimensions by representing a group of pixels with their average, as in Equation (3) [ 30 ].

where m, n refer to the placement in the matrix, p refers to the step of filter, f refers to the size of the filter, and k refers to the features vectors.…”

Section: Methodsmentioning

confidence: 99%

Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

et al. 2023

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Lung and colon cancer are among humanity’s most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.

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“…This section presents a novel method for diagnosing tuberculosis using X-ray images by integrating features extracted from deep learning models (ResNet-50 and GoogLeNet) with algorithms (GLCM, DWT, and LBP) and classifying them using an ANN classifier [40]. The steps to implement the proposed method are as follows: first, we feed the enhanced X-ray images into the ResNet-50 and GoogLeNet models.…”

Section: The Ann Classifier Based On Fusion Of Featuresmentioning

confidence: 99%

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

2022

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Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.

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Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases

Cited by 29 publications

References 47 publications

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

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