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Examples of vascular structures seen in dermoscopic images of skin lesions: a linear b dotted c hairpin d linear crown e arborizing f polymorphous vessels. Some of these structures give very specific diagnostic clues as in (d) the presence of crown vessels (linear straight) distributed on the periphery of the lesion surrounded by yellowish globular structures is a specific marker for sebaceous hyperplasia
Source publication
Vascular structures of skin are important biomarkers in diagnosis and assessment of cutaneous conditions. Presence and distribution of lesional vessels are associated with specific abnormalities. Therefore, detection and localization of cutaneous vessels provide critical information towards diagnosis and stage status of diseases. However, cutaneous...
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Background and Objective
: Early identification of melanoma is conducted through whole-body visual examinations to detect suspicious pigmented lesions, a situation that fluctuates in accuracy depending on the experience and time of the examiner. Computer-aided diagnosis tools for skin lesions are typically trained using pre-selected single-lesion i...
The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however,...
Melanoma is the deadliest type of skin cancer. It has been rising exponentially for the last few decades. If it is diagnosed and treated at its early stage, the survival rate is very high. To prevent the invasive biopsy technique, automated diagnosis of melanoma from dermoscopy images has become a hot research area for the last few decades. This pa...
Background:
Malignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history. However, there is controversy over screening programs and many advocate screening only for high-risk individuals.
Objectives:
This study aimed to evaluate the accuracy of an artificial intelligence neural network (Deep Ensemble...
Citations
... CNNs, conversely, include feature extraction in their workflow: given an input image or ROI, they extract the most informative features and then exploit these features for classification using the abovementioned MLP (often referred as "dense layers") [10]. For this reason, CNNs are widely used in medical image analysis [51][52][53]. ...
An increasingly strong connection between artificial intelligence and medicine has enabled the development of predictive models capable of supporting physicians’ decision-making. Artificial intelligence encompasses much more than machine learning, which nevertheless is its most cited and used sub-branch in the last decade. Since most clinical problems can be modeled through machine learning classifiers, it is essential to discuss their main elements. This review aims to give primary educational insights on the most accessible and widely employed classifiers in radiology field, distinguishing between “shallow” learning (i.e., traditional machine learning) algorithms, including support vector machines, random forest and XGBoost, and “deep” learning architectures including convolutional neural networks and vision transformers. In addition, the paper outlines the key steps for classifiers training and highlights the differences between the most common algorithms and architectures. Although the choice of an algorithm depends on the task and dataset dealing with, general guidelines for classifier selection are proposed in relation to task analysis, dataset size, explainability requirements, and available computing resources. Considering the enormous interest in these innovative models and architectures, the problem of machine learning algorithms interpretability is finally discussed, providing a future perspective on trustworthy artificial intelligence.
Relevance statement The growing synergy between artificial intelligence and medicine fosters predictive models aiding physicians. Machine learning classifiers, from shallow learning to deep learning, are offering crucial insights for the development of clinical decision support systems in healthcare. Explainability is a key feature of models that leads systems toward integration into clinical practice.
Key points
• Training a shallow classifier requires extracting disease-related features from region of interests (e.g., radiomics).
• Deep classifiers implement automatic feature extraction and classification.
• The classifier selection is based on data and computational resources availability, task, and explanation needs.
Graphical Abstract
... The CNN is a type of DL algorithm consisting of interconnected layers of neurons that respond to different visual stimuli. It has demonstrated its robustness and versatility in various regression and classification tasks, e.g., fault classification, image processing, time series modeling, and feature extraction (Kharazmi et al., 2018;Rahimilarki et al., 2022;Shu et al., 2021;Weimer et al., 2016). ...
The significance of understanding the flow past a bluff body (BB) lies in its relevance to ocean, structural, and environmental applications. Capturing the transient flow behaviors with fine details requires extensive computational power. To address this, the present study develops an improved method for modeling the complex flow dynamics around a BB under steady and unsteady conditions. It is a deep learning (DL)-enhanced reduced-order model (ROM) that leverages the strengths of proper orthogonal decomposition (POD) for model reduction, convolutional neural network long short-term memory (CNN-LSTM) for feature extraction and temporal modeling, and Bayesian optimization for hyperparameter tuning. The model starts with dimensionality reduction, followed by DL optimization and forecasting, and terminates with flow field reconstruction by combining dominant POD modes and predicted amplitudes. The goal is to establish a DL-driven ROM for fast and accurate modeling of the flow evolution. Based on the comparison of millions of data samples, the predictions from the ROM and CFD are considerably consistent, with a coefficient of determination of 0.99. Furthermore, the ROM is ~10 times faster than the CFD and exhibits a robust noise resistance capability. This study contributes to a novel modeling approach for complex flows, enabling rapid decision-making and interactive visualization.
... In the literature, most of the studies on dermoscopic findings of cutaneous vasculitis focused on the differential diagnosis of common urticaria and urticarial vasculitis findings. We also evaluated dermoscopic data with RF, Decision Tree, and KNN, which are the mostly used ML classification methods [26]. We achieved over 80% accurate diagnosis of CV with RF (88%) and Decision Tree (82%). ...
... In our literature review, no study was found in which vasculitis dermoscopy was combined with AI. However, there were studies in which the dermoscopy of vascular structures, which has an important place in vasculitis dermoscopy, was evaluated with AI [26,30]. In our study, RF and decision tree classifiers, which are ML methods, highlighted background color, scales, red dots, and comma vessels as distinctive dermoscopic findings in the classification of purpuric lesions. ...
Introduction
Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable technique is required for the diagnosis.
Objectives
We aimed to define CV dermoscopic features and increase the diagnostic accuracy of dermoscopy with machine learning (ML) methods.
Methods
Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were independently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and K-Nearest Neighbors were used as ML classification models.
Results
The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homogeneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensitivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern (κ = 0.869) and background color analysis (κ = 0.846) (P < 0.001). According to ML classifiers, the background color and lack of scales were the most significant dermoscopic aspects of CV.
Conclusions
Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accuracy rates obtained with ML methods may increase the success of dermoscopy.
... Kharazmi et al. [15] applied independent component analysis, k-means clustering, and shape for detecting vessels and other vascular structures. Kharazmi et al. [16] detected vessel patches by using a stacked sparse autoencoder (SSAE) as their DL model. Maurya et al. [17] employed DL to segment these vessels semantically, a dermoscopic-featuredriven approach also used by Nambisan et al. to detect dots and globules [18]. ...
A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.
... Durch eine automatische Registrierung mittels tiefer neuronaler Netze und durch den Einsatz eines akustischen Feedbacks sollen diese Probleme gelöst werden. Tiefe neuronale Netze als modernes Verfahren maschinellen Lernens haben in den letzten Jahren in der medizinischen BildanalyseAnwendunggefunden [10,21]. ...
Zusammenfassung
Hintergrund
Der Ultraschall als wichtiges Bildgebungsverfahren im Kopf-Hals-Bereich ist leicht verfügbar, dynamisch, kostengünstig und ohne Strahlenbelastung. Eingriffe in der komplexen Kopf-Hals-Anatomie erfordern eine gute Orientierung, die durch Navigationssysteme unterstützt wird.
Ziel der Arbeit
Entwicklung eines neuen ultraschallkontrollierten Navigationssystems zur Punktion kleiner Zielstrukturen im Kopf-Hals-Bereich.
Methodik
Es wurde ein Halsphantom mit sonographierbaren Raumforderungen (RF; Größe: 8–10 mm) konstruiert. Diese wurden automatisch mittels eines ResNet-50-basierten tiefen neuronalen Netzes segmentiert. Der Ultraschallkopf (UK) wurde mit einem individuell hergestellten Trackingtool versehen.
Ergebnisse
Die Positionen von Ultraschallgerät, RF und Punktionsnadel wurden im Weltkoordinatensystem erfasst. In 8 von 10 Fällen wurde eine 8 mm große RF getroffen. Die durchschnittliche Abweichung wurde mit 2,5 mm in einem speziellen Evaluationsphantom berechnet. Die getrackte Biopsienadel wird durch auditives Feedback ausgerichtet und zur RF navigiert.
Schlussfolgerung
Herausragende Vorteile im Vergleich zu herkömmlichen Navigationssystemen sind: Verzicht auf präoperative Schnittbildgebung, automatische dreidimensionale Echtzeitregistrierung, welche die intraoperative Gewebeverschiebungen berücksichtigt, Beibehaltung der optischen Achse des Operateurs auf den Situs, ohne dass auf einen Navigationsmonitor geschaut werden muss, und beidhändiges Arbeiten ohne Halten des UK während der Punktion. Insgesamt lässt sich das beschriebene Funktionsmuster außer für Nadelbiopsien auch in der offenen Kopf-Hals-Chirurgie anwenden.
... Furthermore, DL does not need any kind of segmentation. Deep CNN consists of convolution, pooling, and fully connected layers [12,13]. DL needs huge amount of data for training and testing process, therefore transfer learning (TL) is an applicable solution in case of small amount of data. ...
Diabetic retinopathy (DR) is a chronic eye disease and the main reason of blindness between adults. Manual DR screening methods require skilled readers, effort, and time. Automated DR detection and classification of its severity is important for early and effective disease control. Mobile devices can facilitate regular screening of retinal fundus images using automated lightweight architectures techniques. While, many automated techniques have been proposed in the literature, new models are needed to improve the accuracy and to facilitate the accessibility to such models. In this study, MobileNetV2 is used to classify five classes of DR severity that includes no DR, mild DR, moderate DR, severe DR, and proliferative DR. Colour fundus images are enhanced using contrast-limited adaptive histogram equalization (CLAHE) to defeat bad lighting conditions. MobileNetV2 is used to extract image features. A new classification head based on batch normalization and L2 regularization is applied on the extracted features. The model is tested using augmented data, non-augmented data, fine-tuning, and without fine-tuning. The experiments indicate the importance of augmentation and fine-tuning for improving the evaluation metrics. The experimental results show the superiority of the proposed technique compared to recent techniques in literature with training, validation, and test accuracies of 98.65, 87.94%, and 87.67% respectively.
... Premaladha and Ravichandran have suggested an efficient integrated model of deep learning and Adaboost-SVM for predicting and classifying the melanoma disease (Premaladha and Ravichandran 2016). For identification of cutaneous vessels, Kharazmi et al. have suggested a data-driven feature learning framework based on stacked sparse auto-encoders (SSAE) (Kharazmi et al. 2018). Wang et al. have created an 8-layer CNN with optimal structure and max pooling to classify the Alzheimer's disease (Wang et al. 2018b). ...
In the past few years, the performance of deep learning is quite recognizable in the biomedical image processing domain. The convolutional neural networks (CNNs) models are proved to be excellent in diagnosing the biomedical images. However, the conventional CNN approaches have reached their ceiling on performance. Nevertheless, the performance of the CNN model can be improved in classification task. On the contrary, biomedical image dataset quality has a substantial influence on the performance of the model. It is noted that the contrast of images is not sufficient because of few factors like illumination, camera, and angle and as a result insufficient image features are generated. Therefore, enhancing the image is a crucial part of pre-processing through which the performance of CNN models can be effectually improved. Consequently, this paper recommends how to integrate the CNN model with illumination normalization techniques to achieve better classification accuracy on MNIST medical dataset. This work has selected mobileNetV2 and squeezeNet conventional networks and combined with Tantrigg and Isotropic illumination enhancement techniques. Among all the methods, the best result of 99.13% is obtained for Tantrigg integrated with traditional CNN models. Similarly, the best result of 98.20% is obtained for Isotropic integrated with traditional CNN models. Extensive experiments and results demonstrate that the suggested method is efficient to deal with non-uniform illuminated images and deliver better classification accuracy.
... Machine learning (ML) methods have recently been investigated in the field of dermatology, and the majority of developed algorithms are diagnostic binary classifiers [11,12]. A number of studies have evaluated the performance of algorithms developed to detect specific dermoscopic features, including pigment network structures, vessels, and blue-white veil; however, many algorithms were trained and tested on relatively small data sets and have achieved only moderate accuracy [13][14][15][16][17][18][19][20][21]. ...
Background
Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images.
Objective
The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts.
Methods
First, we obtained labels of 248 images of melanocytic lesions with 31 dermoscopic “subfeatures” labeled by 20 dermoscopy experts. These were then collapsed into 6 dermoscopic “superfeatures” based on structural similarity, due to low interrater reliability (IRR): dots, globules, lines, network structures, regression structures, and vessels. These images were then used as the gold standard for the crowd study. The commercial platform DiagnosUs was used to obtain annotations from a nonexpert crowd for the presence or absence of the 6 superfeatures in each of the 248 images. We replicated this methodology with a group of 7 dermatologists to allow direct comparison with the nonexpert crowd. The Cohen κ value was used to measure agreement across raters.
Results
In total, we obtained 139,731 ratings of the 6 dermoscopic superfeatures from the crowd. There was relatively lower agreement for the identification of dots and globules (the median κ values were 0.526 and 0.395, respectively), whereas network structures and vessels showed the highest agreement (the median κ values were 0.581 and 0.798, respectively). This pattern was also seen among the expert raters, who had median κ values of 0.483 and 0.517 for dots and globules, respectively, and 0.758 and 0.790 for network structures and vessels. The median κ values between nonexperts and thresholded average–expert readers were 0.709 for dots, 0.719 for globules, 0.714 for lines, 0.838 for network structures, 0.818 for regression structures, and 0.728 for vessels.
Conclusions
This study confirmed that IRR for different dermoscopic features varied among a group of experts; a similar pattern was observed in a nonexpert crowd. There was good or excellent agreement for each of the 6 superfeatures between the crowd and the experts, highlighting the similar reliability of the crowd for labeling dermoscopic images. This confirms the feasibility and dependability of using crowdsourcing as a scalable solution to annotate large sets of dermoscopic images, with several potential clinical and educational applications, including the development of novel, explainable ML tools.
... Thus, CBC may not be adequate or fast enough to meet the demand of doctors when screening for anemia patients fast and accurately, especially in mass casualty incidents such as war settings. With the rapid development of technology, noninvasive facial recognition technology has been widely used in medicine, such as the area of diagnosis of genetic disorder diseases diagnosis (17, 18), the area of diagnosis of dermatological diseases diagnosis (19,20), the area of nervous system diseases (21,22), etc. Researchers have been studying mucous membrane color changes as a potential biomarker for rapid and reliable anemia diagnosis using facial recognition technology in recent years (23)(24)(25). ...
Background
According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological mechanism by which anemia can affect facial characteristics, such as membrane pallor, has been proven to detect anemia with the help of deep learning technology. The quick prediction method for the patient in the emergency department is important to screen the anemic state and judge the necessity of blood transfusion treatment.
Method
We trained a deep learning system to predict anemia using videos of 316 patients. All the videos were taken with the same portable pad in the ambient environment of the emergency department. The video extraction and face recognition methods were used to highlight the facial area for analysis. Accuracy and area under the curve were used to assess the performance of the machine learning system at the image level and the patient level.
Results
Three tasks were applied for performance evaluation. The objective of Task 1 was to predict patients' anemic states [hemoglobin (Hb) <13 g/dl in men and Hb <12 g/dl in women]. The accuracy of the image level was 82.37%, the area under the curve (AUC) of the image level was 0.84, the accuracy of the patient level was 84.02%, the sensitivity of the patient level was 92.59%, and the specificity of the patient level was 69.23%. The objective of Task 2 was to predict mild anemia (Hb <9 g/dl). The accuracy of the image level was 68.37%, the AUC of the image level was 0.69, the accuracy of the patient level was 70.58%, the sensitivity was 73.52%, and the specificity was 67.64%. The aim of task 3 was to predict severe anemia (Hb <7 g/dl). The accuracy of the image level was 74.01%, the AUC of the image level was 0.82, the accuracy of the patient level was 68.42%, the sensitivity was 61.53%, and the specificity was 83.33%.
Conclusion
The machine learning system could quickly and accurately predict the anemia of patients in the emergency department and aid in the treatment decision for urgent blood transfusion. It offers great clinical value and practical significance in expediting diagnosis, improving medical resource allocation, and providing appropriate treatment in the future.
... CNN is a deep learning algorithm that employs a hierarchical topology of connections inspired by the biological visual system, which allows it to learn the mapping of data features to categories (15). Recently, convolutional neural network has been applied more and more in the field of visual system and medical image analysis (16)(17)(18). A study showed that two different Convolutional Neural Networks could accurately classify 77.3 and 77.9% of the test atlas and the diagnostic accuracy of the test atlas was as high as 90.4 and 89.7%. ...
Ultrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is an advanced computer program that can solve this problem. Therefore, the relevant literature on the application of artificial intelligence in the ultrasonic field from 2011 to 2021 was screened by authors from the Web of Science Core Collection, which aims to summarize the trend of artificial intelligence application in the field of ultrasound, meanwhile, visualize and predict research hotspots. A total of 908 publications were included in the study. Overall, the number of global publications is on the rise, and studies on the application of artificial intelligence in the field of ultrasound continue to increase. China has made the largest contribution in this field. In terms of institutions, Fudan University has the most number of publications. Recently, IEEE Access is the most published journal. Suri J. S. published most of the articles and had the highest number of citations in this field (29 articles). It's worth noting that, convolutional neural networks (CNN), as a kind of deep learning algorithm, was considered to bring better image analysis and processing ability in recent most-cited articles. According to the analysis of keywords, the latest keyword is “COVID-19” (2020.8). The co-occurrence analysis of keywords by VOSviewer visually presented four clusters which consisted of “deep learning,” “machine learning,” “application in the field of visceral organs,” and “application in the field of cardiovascular”. The latest hot words of these clusters were “COVID-19; neural-network; hepatocellular carcinoma; atherosclerotic plaques”. This study reveals the importance of multi-institutional and multi-field collaboration in promoting research progress.
