Diagnosis of mesothelioma with deep learning

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful "Malignant Mesothelioma illness" identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support "Vector Machine, Neural Network, and Decision Tree" are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.

Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure

Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research.

Informative Power Evaluation of Clinical Parameters to Predict Initial Therapeutic Response in Patients with Advanced Pleural Mesothelioma: A Machine Learning Approach

Malignant pleural mesothelioma (MPM) is a rare neoplasm whose early diagnosis is challenging and systemic treatments are generally administered as first line in the advanced disease stage. The initial clinical response may represent a useful parameter in terms of identifying patients with a better long-term outcome. In this report, the initial therapeutical response in 46 patients affected with advanced/unresectable pleural mesothelioma was investigated. The initial therapeutic response was assessed by CT scan and clinical examination after 2–3 treatment cycles. Our preliminary evaluation shows that the group of patients treated with regimens including antiangiogenetics and/or immunotherapy had a significantly better initial response as compared to patients only treated with standard chemotherapy, exhibiting a disease control rate (DCR) of 100% (95% IC, 79.40–100%) and 80.0% (95% IC, 61.40–92.30%), respectively. Furthermore, the therapeutic response was correlated with the disease stage, blood leukocytes and neutrophils, high albumin serum levels, and basal body mass index (BMI). Specifically, the patients with disease stage III showed a DCR of 95.7% (95% IC, 78.1–99.9%), whereas for disease stage IV the DCR decreased to 66.7% (95% IC, 34.9–9.1%). Moreover, a better initial response was observed in patients with a higher BMI, who reached a DCR of 96.10% (95% IC, 80.36–99.90%). Furthermore, in order to evaluate in the predictive power of the collected features a multivariate way, we report the preliminary results of a machine learning model for predicting the initial therapeutic response. We trained a state-of-the-art algorithm combined to a sequential forward feature selection procedure. The model reached a median AUC value, accuracy, sensitivity, and specificity of 77.0%, 75%, 74.8%, and 83.3%, respectively. The features with greater informational power were gender, histotype, BMI, smoking habits, packs/year, and disease stage. Our preliminary data support the possible favorable correlation between innovative treatments and therapeutic response in patients with unresectable/advanced pleural mesothelioma. The small sample size does not allow concrete conclusions to be drawn; nevertheless, this work is the basis of an ongoing study that will also involve radiomics in a larger dataset.

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.