Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

Ahmed, Ibrahim Abdulrab; Senan, Ebrahim Mohammed; Shatnawi, Hamzeh Salameh Ahmad; Alkhraisha, Ziad Mohammad; Al-Azzam, Mamoun Mohammad Ali

doi:10.3390/diagnostics13040814

Cited by 23 publications

(17 citation statements)

References 49 publications

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“…The RF is based on ensemble learning by combining the results of several classifiers and making a decision based on the majority vote. The classifier’s name suggests the many decision tree classifiers from subsets of the original dataset and takes the average prediction accuracy for all trees rather than relying on a single tree [ 49 ]. The higher the number of the decision tree, the more efficient and reliable the accuracy.…”

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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“…Studies on the marine lives detection using deep learning techniques are summarized in [24], [25]. Also, early diagnosis of skin cancer from skin lesions using hybrid models CNN-ANN and CNN-RF is presented [14]. A survey in this direction has been studied in [26].…”

Section: Related Workmentioning

confidence: 99%

“…Before the deep learning era, different local shape and texture information including the LBP, geometric properties, shape profiles, bag of words, Scale Invariant Feature Transform (SIFT), colors, and many more have been described in the literature [32], [33], [13], [34], [26], [14], [35], [36]. Most of these conventional feature descriptors are used for recognizing the human faces, emotions, hand-shape, palmprint, skin lesions, and other biometric modality and object categories.…”

Section: Related Workmentioning

confidence: 99%

“…We have experimented on eight small-scale image datasets (1k-15k), representing a wide variations in the object's shape, color, background, texture, etc. The datasets includes human faces with age-variations [11], [12]; hand shapes/palmprint [13], skin lesions [14], natural objects like flowers, underwater sea-lives; and food-dishes of India [15] and Thailand [16]. This paper is an improvement of our earlier published work [17].…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks Fused with Textures for Image Classification

Bera

Bhattacharjee

Nasipuri

2023

Lecture Notes in Networks and Systems

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Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modeling using the long shortterm memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.

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“…Ahmad et al developed hybrid techniques SVM-MobileNet, SVM-ResNet101and SVM-MobileNet-ResNet101 to classify two datasets, HAM10000 and PH2, of skin lesions. Their results showed better performance than pre-trained CNN models[16]. Alwakid et al employed Inception-V3 and Inception Resnet-V2 models for melanoma recognition using the HAM10000 dataset.…”

mentioning

confidence: 99%

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

SÖNMEZ

ÇAKAR

Selamet

et al. 2023

Sakarya University Journal of Computer and Information Sciences

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Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches, the study aimed to identify the most effective strategies for accurate skin disease classification in dermoscopic images.

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Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

Cited by 23 publications

References 49 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

Deep Neural Networks Fused with Textures for Image Classification

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

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