Optimized Swarm Enabled Deep Learning Technique for Bone Tumor Detection using Histopathological Image

Cancer subjugates a community that lacks proper care. It remains apparent that research studies enhance novel benchmarks in developing a computer-assisted tool for prognosis in radiology yet an indication of illness detection should be recognized by the pathologist. In bone cancer (BC), Identification of malignancy out of the BC’s histopathological image (HI) remains difficult because of the intricate structure of the bone tissue (BTe) specimen. This study proffers a new approach to diagnosing BC by feature extraction alongside classification employing deep learning frameworks. In this, the input is processed and segmented by Tsallis Entropy for noise elimination, image rescaling, and smoothening. The features are excerpted employing Efficient Net-based Convolutional Neural Network (CNN) Feature Extraction. ROI extraction will be employed to enhance the precise detection of atypical portions surrounding the affected area. Next, for classifying the accurate spotting and for grading the BTe as typical and a typical employing augmented XGBoost alongside Whale optimization (WOA). HIs gathering out of prevailing scales patients is acquired alongside texture characteristics of such images remaining employed for training and testing the Neural Network (NN). These classification outcomes exhibit that NN possesses a hit ratio of 99.48 percent while this occurs in BT classification

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed

Künstliche Intelligenz in der Zahnheilkunde: Scoping-Review und Schließung beobachteter Wissenslücken durch eine methodische und eine klinische Studie

Objectives: The aims of this dissertation were to (1) conduct a scoping review of stud-ies on machine learning (ML) in dentistry and appraise their robustness, (2) perform a benchmarking study to systematically compare various ML algorithms for a specific dental task, and (3) evaluate the influence of a ML-based caries detection software on diagnostic accuracy and decision-making in a randomized controlled trial. Methods: The scoping review included studies using ML in dentistry published between 1st January 2015 and 31st May 2021 on MEDLINE, IEEE Xplore, and arXiv. The risk of bias and reporting quality were assessed with the QUADAS‐2 and TRIPOD checklists, respectively. In the benchmarking study, 216 ML models were built using permutations of six ML model architectures (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network, and Mask Attention Network), 12 model backbones of varying complexities (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169, and Dense-Net201), and three initialization strategies (random, ImageNet, and CheXpert weights). 1,625 dental bitewing radiographs were used for training and testing. Five-fold cross-validation was carried out and model performance assessed using F1-score. In the clin-ical trial, each one of 22 dentists examined 20 randomly selected bitewing images for proximal caries; 10 images were evaluated with ML and 10 images without ML. Accura-cy in lesion detection and the suggested treatment were evaluated. Results: The scoping review included 168 studies, describing different ML tasks, mod-els, input data, methods to generate reference tests, and performance metrics, imped-ing comparison across studies. The studies showed considerable risk of bias and mod-erate adherence to reporting standards. In the benchmarking study, more complex models only minimally outperformed their simpler counterparts, if at all. Models initial-ized by ImageNet or CheXpert weights outperformed those using random weights (p<0.05). The clinical trial demonstrated that dentists using ML showed increased accu-racy (area under the receiver operating characteristic [mean (95% confidence interval): 0.89 (0.87–0.90)]) compared with those not using ML [0.85 (0.83–0.86); p<0.05], pri-marily due to their higher sensitivity [0.81 (0.74–0.87) compared to 0.72 (0.64–0.79); p<0.05]. Notably, dentists using ML also showed a higher frequency of invasive treat-ment decisions than those not using it (p<0.05). Conclusion: To facilitate comparisons across ML studies in dentistry, a minimum (core) set of outcomes and metrics should be developed, and researchers should strive to improve robustness and reporting quality of their studies. ML model choice should be performed on an informed basis, and simpler models may often be similarly capable as more complex ones. ML can increase dentists’ diagnostic accuracy but also lead to more invasive treatment.Ziele: Die Ziele dieser Dissertation waren, (1) ein Scoping-Review von Studien über maschinelles Lernen (ML) in der Zahnmedizin, (2) eine Benchmarking-Studie zum systematischen Vergleich verschiedener ML-Algorithmen für eine bestimmte zahnmedizinische Aufgabe, und (3) eine randomisierte kontrollierte Studie zur Bewertung einer ML-basierten Karies-Erkennungssoftware bezüglich diagnostischer Genauigkeit und Einfluss auf den Entscheidungsprozess durchzuführen. Methoden: Das Scoping-Review umfasste Studien über ML in der Zahnmedizin, veröffentlicht vom 1. Januar 2015 bis 31. Mai 2021 auf MEDLINE, IEEE Xplore und arXiv. Bias-Risiko und Berichtsqualität wurden mit den Checklisten QUADAS-2 beziehungsweise TRIPOD bewertet. In der Benchmarking-Studie wurden 216 ML-Modelle durch Permutationen von sechs Architekturen (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network und Mask Attention Network), 12 Backbones (Res-Net18, ResNet34, ResNet50, ResNet101, ResNet152, VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169 und DenseNet201) und drei Initialisierungsstrategien (zufällige-, ImageNet- und CheXpert-Gewichtungen) erstellt. Zum Training und Testen wurden 1.625 Bissflügel-Röntgenaufnahmen genutzt. Es wurde eine fünffache Kreuzvalidierung durchgeführt und die Modellleistung anhand des F1-Scores bewertet. In der klinischen Studie untersuchten 22 Zahnärzte jeweils 20 zufällig ausgewählte Bissflügelbilder auf Approximalkaries; 10 Bilder wurden mit und 10 Bilder ohne ML ausgewertet. Die Genauigkeit in der Erkennung von Läsionen sowie die abgeleitete Therapieempfehlung wurden bewertet. Ergebnisse: Das Scoping-Review schloss 168 Studien ein, in denen verschiedene ML-Aufgaben, Modelle, Eingabedaten, Methoden zur Generierung von Referenztests und Leistungsmetriken beschrieben wurden. Die Studien zeigten ein erhebliches Bias-Risiko und eine mäßige Einhaltung der Berichtsstandards. In der Benchmarking-Studie hatten komplexere Modelle gegenüber einfachen Modellen allenfalls geringe Vorteile. Mit ImageNet- oder CheXpert-Gewichtungen initialisierte Modelle übertrafen solche mit Zufallsgewichtungen (p<0,05). In der klinischen Studie erreichten Zahnärzte mit ML eine höhere Genauigkeit bei der Kariesdetektion (Receiver-Operating-Charakteristik [Mittelwert (95 % Konfidenzintervall) 0,89 (0,87–0,90)]) als ohne ML [0,85 (0,83–0,86); p<0,05], hauptsächlich aufgrund höherer Sensitivität [0,81 (0,74–0,87) verglichen mit 0,72 (0,64–0,79); p<0,05]. Zahnärzte mit ML wählten auffallend häufiger invasive Behandlungen als ohne ML (p<0,05). Schlussfolgerung: Zur besseren Vergleichbarkeit von ML-Studien in der Zahnmedizin, sollten Core Outcomes und Metriken definiert sowie Robustheit und Berichtsqualität verbessert werden. Die Entwicklung von ML-Modellen sollte auf informierter Basis erfolgen, bei oft ähnlicher Leistung von einfacheren und komplexeren Modellen. ML kann die diagnostische Genauigkeit erhöhen, aber auch zu mehr invasiven Behandlungen führen

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods