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Illustrations of various types of nontraining nodules and nonnodules and corresponding output images of the trained MTANN. Nodules are represented by bright pixels, whereas nonnodules are almost dark around the centers of ROIs.
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Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of...
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Citations
... All of the procedures rely on radiography, a once-outdated medical imaging technique. However, developments in digital technology of machine learning and have resurrected this procedure (Arbib, 2003) and its significance in the diagnosis of pulmonary illness (Suzuki, 2012). In particular, they enable the detection of various cardiothoracic lessons on x-ray imaging. ...
This thesis focuses on the importance of early detection in lung cancer through the use of medical imaging techniques and deep learning models. The current practice of examining nodules larger than 7 mm can delay detection and allow cancerous nodules to grow undetected. The project aims to detect nodules as small as 3 mm to improve the chances of early cancer identification. The use of constrained volume datasets and transfer learning techniques addresses the scarcity of medical data, and deep neural networks are employed for classification and segmentation tasks. Despite the limited dataset, the results demonstrate the effectiveness of the proposed models. Class activation maps and segmentation techniques enhance accuracy and provide insights into the most critical areas for diagnosis. This research contributes to the understanding of lung disease diagnosis and highlights the potential of deep learning in medical imaging.
... First, the NCCT DICOM images were converted to JPEG (joint photographic expert group) using the software Radi-Ant DICOM Viewer 26 with the maintenance of the original image dimension 512 × 512 and the standard 8-bit grayscale depth (0-255). A pixel-level analysis was performed instead of voxel-level for which 2D NCCT slices were preferred 27 . The distortion of brain tissue was carefully prevented after the conversion of NCCT images. ...
Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present study, we developed a deep learning (DL) localization model to help localize the early infarction sign on NCCT. This retrospective study included consecutive 517 ischemic stroke (IS) patients who received NCCT within 12 h after stroke onset. A total of 21,436 infarction patches and 20,391 non-infarction patches were extracted from the slice pool of 1,634 NCCT according to brain symmetricity property. The generated patches were fed into different pretrained convolutional neural network (CNN) models such as Visual Geometry Group 16 (VGG16), GoogleNet, Residual Networks 50 (ResNet50), Inception-ResNet-v2 (IR-v2), Inception-v3 and Inception-v4. The selected VGG16 model could detect the early infarction in both supratentorial and infratentorial regions to achieve an average area under curve (AUC) 0.73 after extensive customization. The properly tuned-VGG16 model could identify the early infarction in the cortical, subcortical and cortical plus subcortical areas of supratentorial region with the mean AUC > 0.70. Further, the model could attain 95.6% of accuracy on recognizing infarction lesion in 494 out of 517 IS patients.
... Six models with feature maps of 16,20,25,30,35, and 64 pixels each. A maximum pooling layer with a window size of 2 × 2 is paired with batch normalization on the output of the suggested (OLReLU) in the first five layers. ...
... There are typical methods for feature extraction from handwritten character images: Pixel-based features: In this approach, each pixel in the image is considered a feature. [15] [16]. Edge-based features: The edges of the image can be detected using techniques such as the Canny edge detector [17]. ...
Recognizing and writing Arabic characters correctly is a difficult task, especially for non-native speakers. With the growing trend toward digital teaching and distance learning, there is a need for efficient and accurate automatic recognition of Arabic characters. The work investigates three distinct approaches to address this challenge. Firstly, features are extracted from handwritten Arabic character images using two pre-trained models: EfficientNet B2 and DenseNet 201. These extracted features are then employed for classification utilizing various classifiers. Secondly, the features obtained from both models are concatenated to enhance classification performance by leveraging the unique representations learned by each model. Lastly, the Particle Swarm Optimization (PSO) algorithm is integrated into the feature selection process, identifying the most relevant features that contribute to improved classification accuracy. The best test accuracies obtained were 85.94% in the first experiment, 88.73% in the second, and 90.20% in the third.
... The most important challenges in the medical domain are the analysis of biomedical data or medical images, detection or diagnostic of certain diseases as well as the extraction of understandable knowledge and patterns from medical imaging or diagnosis data [1] because such objects may be too complicated to be represented correctly by a simple equation [2]. ...
... The pixel-based examinations (Suzuki, 2012) are used by the author to examine the medical images for appropriate diseases. It uses distinct values in image chunks instead from feature extraction. ...
In driving the medical image research machine-learning and deep-learning algorithms are growing expeditiously. The premature conjecture of disease needs substantial attempts to diagnose the disease. The machine learning algorithm confesses the software application to study from the data and predicts more accurate outcome. The deep learning algorithm drives on extensive dataset imparts on high end machine and clarifies the problem end to end. The primary focus on the survey is to high-spots the machine and deep-learning approaches in medical image analysis that endorses the decision-making practices. The paper provides a plan for the researchers to perceive the extant schemes sustained out for medical imaging with its recognition and hindrances of the machine and deep learning algorithm.
... Another example of machine-based extraction of learning features is the use of pixel analysis in machine learning. Instead of using a simple feature-based classifier for a given problem it can be more effective [5]. Due to its low contrast, it is difficult to test the image's properties. ...
... Traditional machine learning vs deep learning[ 5]. ...
... In the past few years, new information technology techniques, such as blockchain [1][2][3][4] and machine-learning [5][6][7][8][9][10][11][12][13][14][15], have been developing rapidly and used successfully in various real-life applications. However, they still face a critical challenge in the privacy-preserving issue. ...
In recent years, blockchain and machine-learning techniques have received increasing attention both in theoretical and practical aspects. However, the applications of these techniques have many challenges, one of which is the privacy-preserving issue. In this paper, we focus on, specifically, the privacy-preserving issue of imbalanced datasets, a commonly found problem in real-world applications. Built based on the fully homomorphic encryption technique, this paper presents two new secure protocols, Privacy-Preserving Synthetic Minority Oversampling Protocol (PPSMOS) and Borderline Privacy-Preserving Synthetic Minority Oversampling Protocol (Borderline-PPSMOS). Our analysis reveals that PPSMOS is generally more efficient in performance than Borderline-PPSMOS. However, Borderline-PPSMOS achieves a better TP rate and F-Value than PPSMOS.
... For instance, nonlesions or lesions and normal or abnormal classifications depend on the features of input (i.e., the features of the segmented object). Thus, Suzuki collected and compared the pixel/voxel-based ML (PML), which directly utilized the values of pixel/voxel in the medical images [184]. The contributions of the work are as follows: observing the PMLs to clarify their classes, defining the differences and similarities between PMLs and the ML features, determining the merits and weaknesses of PMLs, and illustrating the PML applications in medical imaging. ...
Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.
... The pixel-based examinations (Suzuki, 2012) are used by the author to examine the medical images for appropriate diseases. It uses distinct values in image chunks instead from feature extraction. ...
In driving the medical image research machine-learning and deep-learning algorithm are growing expeditiously. The premature conjecture of disease needs substantial attempts to diagnose the disease. The machine learning algorithm confesses the software application to study from the data and predicts more accurate outcome. The deep learning algorithm drives on extensive dataset imparts on high end machine and clarifies the problem end to end. The primary focus on the survey is to high-spots the machine and deep-learning approaches in medical image analysis that endorses the decision-making practices. The paper provides a plan for the researchers to perceive the extant schemes sustained out for medical imaging with its recognition and hindrances of the machine and deep learning algorithm.
... A simple mathematical solution or model cannot accurately distinguish lesions and organs in medical image processing. Machine learning use pixel-based investigation of medical images [4]. ML models have been applied to a wide range of tasks. ...
Technological evolution of the recent years has been growth rapidly to a greater extent by deep learning. Conventional classification systems rely on laborious feature engineering process which requires expertise in domain knowledge for data interpretation. On the contrary, deep learning without using a domain expert can extract distinct features from the data. In this study, conduct some preliminary experiments using the deep learning approach to classify Pulmonary Fibrosis based on human lung CT images, pulmonary fibrosis occurs when lung tissue becomes damaged and scarred. Stiff tissue makes it more difficult for the lungs to work properly. As it worsens, the breath becomes progressively more shortest. This study predicts the severity of the decline in lung function based on a CT scan of more than one patient. In other words, well predict the final three Forced vital capacity "FVC" measurements for each patient, as well as a confidence value.
