Review of medical image recognition technologies to detect melanomas using neural networks

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects

In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

Automatische Körperteil-Identifikation in dermatologischen klinischen Bildern durch maschinelles Lernen

Hintergrund

Dermatologische Erkrankungen sind in allen Bevölkerungsgruppen weit verbreitet. Das betroffene Körperteil ist für ihre Diagnose, Therapie und Forschung von Bedeutung. Die automatische Identifizierung der abgebildeten Körperteile in dermatologischen Krankheitsbildern könnte daher die klinische Versorgung verbessern, indem sie zusätzliche Informationen für klinische Entscheidungsalgorithmen liefert, schwer zu behandelnde Bereiche aufdeckt und die Forschung durch die Identifizierung neuer Krankheitsmuster unterstützt.

Patienten und Methodik

In dieser Studie wurden 6219 annotierte dermatologische Bilder aus unserer klinischen Datenbank verwendet, womit ein neuronales Netz trainiert und validiert wurde. Als Anwendung wurden mit diesem System qualitative Heatmaps für die Verteilung von Körperteilen bei häufigen dermatologischen Erkrankungen erstellt.

Ergebnisse

Der Algorithmus erreichte eine mittlere balancierte Genauigkeit (Accuracy) von 89% (74,8%–96,5%). Die Fotos von nichtmelanozytärem Hautkrebs betrafen vor allem das Gesicht und den Oberkörper, während die größte Häufigkeit der Ekzem‐ und Psoriasis‐Bildverteilung den Oberkörper, die Beine und die Hände umfassten.

Schlussfolgerungen

Die Genauigkeit dieses Systems ist vergleichbar mit den besten bisher veröffentlichten Algorithmen für Bildklassifizierungsaufgaben, was darauf hindeutet, dass dieser Algorithmus die Diagnose, Therapie und Forschung bei dermatologischen Erkrankungen verbessern könnte.

Automatic body part identification in real-world clinical dermatological images using machine learning

BACKGROUND

Dermatological conditions are prevalent across all population sub-groups. The affected body part is of importance to their diagnosis, therapy, and research. The automatic identification of body parts in dermatological clinical pictures could therefore improve clinical care by providing additional information for clinical decision-making algorithms, discovering hard-to-treat areas, and research by identifying new patterns of disease.

PATIENTS AND METHODS

In this study, we used 6,219 labelled dermatological images from our clinical database, which were used to train and validate a convolutional neural network. As a use case, qualitative heatmaps for the body part distribution in common dermatological conditions was generated using this system.

RESULTS

The algorithm reached a mean balanced accuracy of 89% (range 74.8%-96.5%). Non-melanoma skin cancer photos were mostly of the face and torso, while hotspots of eczema and psoriasis image distribution included the torso, legs, and hands.

CONCLUSIONS

The accuracy of this system is comparable to the best to-date published algorithms for image classification challenges, suggesting this algorithm could boost diagnosis, therapy, and research of dermatological conditions.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Melanoma Detection Using Deep Learning-Based Classifications

One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image's quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study's results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

Skin lesion segmentation is the first and indispensable step of malignant melanoma recognition and diagnosis. At present, most of the existing skin lesions segmentation techniques often used traditional methods like optimum thresholding, etc., and deep learning methods like U-net, etc. However, the edges of skin lesions in malignant melanoma images are gradually changed in color, and this change is nonlinear. The existing methods can not effectively distinguish banded edges between lesion areas and healthy skin areas well. Aiming at the uncertainty and fuzziness of banded edges, the neutrosophic set theory is used in this paper which is better than fuzzy theory to deal with banded edge segmentation. Therefore, we proposed a neutrosophy domain-based segmentation method that contains six steps. Firstly, an image is converted into three channels and the pixel matrix of each channel is obtained. Secondly, the pixel matrixes are converted into Neutrosophic Set domain by using the neutrosophic set conversion method to express the uncertainty and fuzziness of banded edges of malignant melanoma images. Thirdly, a new Neutrosophic Entropy model is proposed to combine the three memberships according to some rules by using the transformations in the neutrosophic space to comprehensively express three memberships and highlight the banded edges of the images. Fourthly, the feature augment method is established by the difference of three components. Fifthly, the dilation is used on the neutrosophic entropy matrixes to fill in the noise region. Finally, the image that is represented by transformed matrix is segmented by the Hierarchical Gaussian Mixture Model clustering method to obtain the banded edge of the image. Qualitative and quantitative experiments are performed on malignant melanoma image dataset to evaluate the performance of the NeDSeM method. Compared with some state-of-the-art methods, our method has achieved good results in terms of performance and accuracy.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion

BACKGROUND

Adenocarcinoma and squamous cell carcinoma are the two most prevalent lung cancer types, and their distinction requires different screenings, such as the visual inspection of histology slides by an expert pathologist, the analysis of gene expression or computer tomography scans, among others. In recent years, there has been an increasing gathering of biological data for decision support systems in the diagnosis (e.g. histology imaging, next-generation sequencing technologies data, clinical information, etc.). Using all these sources to design integrative classification approaches may improve the final diagnosis of a patient, in the same way that doctors can use multiple types of screenings to reach a final decision on the diagnosis. In this work, we present a late fusion classification model using histology and RNA-Seq data for adenocarcinoma, squamous-cell carcinoma and healthy lung tissue.

RESULTS

The classification model improves results over using each source of information separately, being able to reduce the diagnosis error rate up to a 64% over the isolate histology classifier and a 24% over the isolate gene expression classifier, reaching a mean F1-Score of 95.19% and a mean AUC of 0.991.

CONCLUSIONS

These findings suggest that a classification model using a late fusion methodology can considerably help clinicians in the diagnosis between the aforementioned lung cancer cancer subtypes over using each source of information separately. This approach can also be applied to any cancer type or disease with heterogeneous sources of information.

Clinical Diagnosis and Classification: Including Biopsy Techniques and Noninvasive Imaging

Early detection of melanoma is important in improving patient survival. The treatment of melanoma is multidisciplinary and begins by obtaining an accurate diagnosis. The mainstays of melanoma diagnosis include examination of the lesion and surrounding areas and an excisional biopsy so that a pathologic diagnosis can be obtained. The pathology results will help guide treatment recommendations, and some information can be used for prognosis. Further workup of the patient may include laboratory studies and imaging for staging and surveillance.

3D-Printed Collagen Scaffolds Promote Maintenance of Cryopreserved Patients-Derived Melanoma Explants

The development of an in vitro three-dimensional (3D) culture system with cryopreserved biospecimens could accelerate experimental research screening anticancer drugs, potentially reducing costs and time bench-to-beside. However, minimal research has explored the application of 3D bioprinting-based in vitro cancer models to cryopreserved biospecimens derived from patients with advanced melanoma. We investigated whether 3D-printed collagen scaffolds enable the propagation and maintenance of patient-derived melanoma explants (PDMEs). 3D-printed collagen scaffolds were fabricated with a 3DX bioprinter. After thawing, fragments from cryopreserved PDMEs (approximately 1–2 mm) were seeded onto the 3D-printed collagen scaffolds, and incubated for 7 to 21 days. The survival rate was determined with MTT and live and dead assays. Western blot analysis and immunohistochemistry staining was used to express the function of cryopreserved PDMEs. The results show that 3D-printed collagen scaffolds could improve the maintenance and survival rate of cryopreserved PDME more than 2D culture. MITF, Mel A, and S100 are well-known melanoma biomarkers. In agreement with these observations, 3D-printed collagen scaffolds retained the expression of melanoma biomarkers in cryopreserved PDME for 21 days. Our findings provide insight into the application of 3D-printed collagen scaffolds for closely mimicking the 3D architecture of melanoma and its microenvironment using cryopreserved biospecimens.

Perceptions of virtual primary care physicians: A focus group study of medical and data science graduate students

BACKGROUND

Artificial and virtual technologies in healthcare have advanced rapidly, and healthcare systems have been adapting care accordingly. An intriguing new development is the virtual physician, which can diagnose and treat patients independently.

METHODS AND FINDINGS

This qualitative study of advanced degree students aimed to assess their perceptions of using a virtual primary care physician as a patient. Four focus groups were held: first year medical students, fourth year medical students, first year engineering/data science graduate students, and fourth year engineering/data science graduate students. The focus groups were audiotaped, transcribed verbatim, and content analyses of the transcripts was performed using a data-driven inductive approach. Themes identified concerned advantages, disadvantages, and the future of virtual primary care physicians. Within those main categories, 13 themes emerged and 31 sub-themes.

DISCUSSION

While participants appreciated that a virtual primary care physician would be convenient, efficient, and cost-effective, they also expressed concern about data privacy and the potential for misdiagnosis. To garner trust from its potential users, future virtual primary physicians should be programmed with a sufficient amount of trustworthy data and have a high level of transparency and accountability for patients.