To date, skin cancer is the most commonly diagnosed form of cancer in humans and is one of the leading causes of death in cancer patients. AI technologies can match and exceed visual analysis methods in accuracy, but they carry the risk of a false negative response when a malignant pigmented lesion can be recognized as benign. Possible ways to improve accuracy and reduce the risk of false negatives are to analyze heterogeneous data, combine different preprocessing methods, and use modified loss functions to eliminate the negative impact of unbalanced dermatological data. The article proposes a multimodal neural network system with a modified cross-entropy loss function that is sensitive to unbalanced heterogeneous dermatological data. The novelty of the proposed system lies in the emerging synergy when using methods to improve the quality of intelligent systems, due to which there is a significant reduction in the number of false negative predictions and an increase in the accuracy of skin cancer recognition. Preliminary cleaning of hair structures on visual data, as well as parallel analysis of heterogeneous dermatological data using a multimodal neural network system sensitive to unbalanced data, were used as methods to improve accuracy. The recognition accuracy for 10 diagnostic categories for the proposed intelligent system was 85.20%. The introduction of weighting factors made it possible to reduce the number of false negative forecasts, as well as increase the accuracy by 1.99-4.28 percentage points compared to the original multimodal systems.

Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.

Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals.

Automatic classification of heart rhythm disturbances using an electrocardiogram is a reliable way to timely detect diseases of the cardiovascular system. The need to automate this process is to increase the number of electrocardiogram signals. Classification methods based on the use of neural networks provide a high percentage of arrhythmia recognition. However, known classification methods do not take into account patient characteristics. The work proposes a multimodal neural network that takes into account the age and gender characteristics of the patient. It includes a Long short-term memory (LSTM) network for feature extraction on twelve-channel electrocardiogram signals and a linear neural network for processing patient metadata such as age and gender. Extraction of electrocardiogram signal features occurs in parallel with metadata processing. The last unifying layer of the proposed multimodal neural network integrates heterogeneous data and features of electrocardiogram signals obtained using an LSTM network. The developed multimodal neural network was verified using the PhysioNet/Computing in Cardiology Challenge 2021 ECG database. The simulation results showed that the proposed multimodal neural network achieves a recognition accuracy of 63%, which is 2 percentage points higher compared to state-of-the-art methods.

Today, skin cancer is one of the leading causes of death in the world. Diagnosing skin cancer early is critical to increasing potential survival. Therefore, it is relevant to develop highprecision intelligent auxiliary diagnostic systems for detecting skin cancer in the early stages. Ensemble learning is one of the current and promising methods for increasing the accuracy of intelligent classification systems by reducing the dispersion and variability of predictions of individual components of the overall system. The work proposes an ensemble intelligent system for analyzing heterogeneous dermatological data based on multimodal neural networks. The accuracy of the developed ensemble system was 85.92 %, which is 1.85 percentage points higher than the average accuracy of individual multimodal architectures for classifying heterogeneous dermatological data. The developed system can be used as a high-precision auxiliary diagnostic tool to help make a medical decision, which will increase the chance of early detection of pigmented oncological pathologies.