Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models.

Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.

Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.

The gastrointestinal system contains the upper and lower gastrointestinal tracts. The main tasks of the gastrointestinal system are to break down food and convert it into essential elements that the body can benefit from and expel waste in the form of feces. If any organ is affected, it does not work well, which affects the body. Many gastrointestinal diseases, such as infections, ulcers, and benign and malignant tumors, threaten human life. Endoscopy techniques are the gold standard for detecting infected parts within the organs of the gastrointestinal tract. Endoscopy techniques produce videos that are converted into thousands of frames that show the disease's characteristics in only some frames. Therefore, this represents a challenge for doctors because it is a tedious task that requires time, effort, and experience. Computer-assisted automated diagnostic techniques help achieve effective diagnosis to help doctors identify the disease and give the patient the appropriate treatment. In this study, many efficient methodologies for analyzing endoscopy images for diagnosing gastrointestinal diseases were developed for the Kvasir dataset. The Kvasir dataset was classified by three pre-trained models: GoogLeNet, MobileNet, and DenseNet121. The images were optimized, and the gradient vector flow (GVF) algorithm was applied to segment the regions of interest (ROIs), isolating them from healthy regions and saving the endoscopy images as Kvasir-ROI. The Kvasir-ROI dataset was classified by the three pre-trained GoogLeNet, MobileNet, and DenseNet121 models. Hybrid methodologies (CNN-FFNN and CNN-XGBoost) were developed based on the GVF algorithm and achieved promising results for diagnosing disease based on endoscopy images of gastroenterology. The last methodology is based on fused CNN models and their classification by FFNN and XGBoost networks. The hybrid methodology based on the fused CNN features, called GoogLeNet-MobileNet-DenseNet121-XGBoost, achieved an AUC of 97.54%, accuracy of 97.25%, sensitivity of 96.86%, precision of 97.25%, and specificity of 99.48%.

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted.

Breast cancer is the second most common type of cancer among women, and it can threaten women's lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient's abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features.

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients' lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

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

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%.

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

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.