Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.

/pdf/impact-of-image-preprocessing-methods-and-deep-learning-1b1an629.pdf

Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio-economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI-enabled medical sensors for off-body detection, near-body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis. 

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Breast cancer (BC) is closely linked with the maximum mortality rate for cancer detection across the globe and has become a predominant public health issue. Earlier detection might increase the possibility of survival and successful treatment. However, it is a time-consuming and very challenging task that depends on the diagnostician’s experience. For patients and their prognosis, it is essential that BC cancer can be automatically detected by the analysis of histopathological images. Conventional feature extraction method extracts some lower-level features of images, and preceding knowledge is essential for selecting suitable features that could be heavily impacted by human beings. The deep learning (DL) technique extracts higher-level abstract features from an image automatically. Therefore, this study develops a new Ensemble Learning with Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification (ELSOSA-BCC) technique on Histopathological Images. In the ELSOSA-BCC technique, the noise is removed using Gabor filtering (GF). In addition, the ELSOSA-BCC technique employs the EfficientNet-B0 model for feature extraction and optimal hyperparameter tuning using the SOS algorithm. Finally, the ensemble learning-based classification process is performed by three classifiers namely deep stacked autoencoder (DSAE), kernel extreme learning machine (KELM), and bidirectional long short-term memory (BiLSTM). In this study, ELSOSA-BCC simulation values are tested on a medical dataset. ELSOSA-BCC has been shown to perform better than other models in the experimental results.

Ensemble Learning With Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification and Risk Identification of Other Organs on Histopathological Images

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

/pdf/applications-of-deep-learning-for-drug-discovery-systems-30et8wab.pdf

Applications of Deep Learning for Drug Discovery Systems with BigData

Background: In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients’ diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. Methods: We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351. Results: Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57–89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05–20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. Conclusion: Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.

https://www.mdpi.com/2075-4426/13/6/962/pdf?version=1686124987

Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study

Abstract The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate ( p &lt; 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists’ performance was not significant ( p &gt; 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports. 

/pdf/identification-of-misdiagnosis-by-deep-neural-networks-on-a-2og317bz.pdf

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases

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Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases