Classification Of Cancer Disease Research Articles

Evaluation of the Gonadotoxicity of Cancer Therapies to Improve Counseling of Patients About Fertility and Fertility Preservation Measures: Protocol for a Retrospective Systematic Data Analysis and a Prospective Cohort Study.

Cytotoxic treatments such as chemo- and radiotherapy and immune therapies are required in cancer diseases. These therapies have the potential to cure patients but may also have an impact on gonadal function and, therefore, on fertility. Consequently, fertility preservation treatments such as freezing of gametes and gonadal tissue might be required. However, as detailed data about the necessity to perform fertility preservation treatment are very limited, this study was designed to fill this data gap. Primary objective of this study is to analyze the impact of cancer therapies and chemotherapies on the ovarian reserve and sperm quality. Secondary objectives are to analyze the (1) impact of cancer therapies and chemotherapies on other fertility parameters and (2) probability of undergoing fertility preservation treatments in relation to specific cancer diseases and treatment protocols and the probability to use the frozen gametes and gonadal tissue to achieve pregnancies. First, previously published studies on the gonadotoxicity of chemo- and radiotherapies among patients with cancer will be systematically analyzed. Second, a prospective cohort study set up by approximately 70 centers in Germany, Switzerland, and Austria will collect the following data: ovarian function by analyzing anti-Müllerian hormone (AMH) concentrations and testicular function by analyzing sperm parameters and total testosterone immediately before and around 1 year after gonadotoxic therapies (short-term fertility). A follow-up of these fertility parameters, including history of conceptions, will be performed 5 and 10 years after gonadotoxic therapies (long-term fertility). Additionally, the proportion of patients undergoing fertility-preserving procedures, their satisfaction with these procedures, and the amount of gametes and gonadal tissue and the children achieved by using the frozen material will be analyzed. Third, the data will be merged to create the internet-based data platform FertiTOX. The platform will be structured in accordance with the ICD (International Classification of Diseases) classification of cancer diseases and will be easily be accessible using a specific App. Several funding bodies have funded this study. Ten systematic reviews are in progress and the first one has been accepted for publication. All Swiss and many German and Austrian ethics committees have provided their approval for the prospective cohort study. The study registry has been set up, and a study website has been created. In total, 50 infertility centers have already been prepared for data collection, which started on December 1, 2023. The study can be expected to bridge the data gap regarding the gonadotoxicity of cancer therapies to better counsel patients about their infertility risk and their need to undergo fertility preservation procedures. Initial data are expected to be uploaded on the FertiTOX platform in 2026. ClinicalTrials.gov NCT05885048; https://clinicaltrials.gov/study/NCT05885048. DERR1-10.2196/51145.

A two-phase cuckoo search based approach for gene selection and deep learning classification of cancer disease using gene expression data with a novel fitness function

A two-phase cuckoo search based approach for gene selection and deep learning classification of cancer disease using gene expression data with a novel fitness function

An Integrated Framework with Deep Learning for Segmentation and Classification of Cancer Disease

This paper addresses radiologists’ specific diagnosis of cancer disease effectively using integrated framework of deep learning model. Although several existing diagnosis systems have been adopted by a physician, in few cases, it is not so practical to see the infected area from images in the normal eye. Thus, a fully integrated diagnosis framework for disease detection is proposed to find out the infected area from image using deep learning approaches in this paper. In this proposed framework, various components are designed through deep learning approaches such as detection, segmentation, classification etc. based on mass region. The classification technique is used to classify the disease as either benign or malignant. The vital part of this framework is developed by using a full resolution convolutional network (FrCN) that supports different stages of image processing, especially breast cancer disease. Different experimental evaluation is taken to perform on the accuracy, cross-validation tests, and the comparative testing. Since we have taken 4-fold evaluation, the FrCN performs with an average 98.7% Dice index, 97.8% TS/CSI coefficient, 99.1% overall accuracy, and 98.15% MCC. Our experiments demonstrated that the proposed diagnosis system performs on the deep learning approaches at each segmentation stage and classification with good results.

A Knowledge-Based System for the Classification of Cancer Diseases in Human Body Using Hybrid Approaches Based on Bipolar Complex Fuzzy Information

A Knowledge-Based System for the Classification of Cancer Diseases in Human Body Using Hybrid Approaches Based on Bipolar Complex Fuzzy Information

A secure IoT based healthcare framework using modified RSA algorithm using an artificial hummingbird based CNN

AbstractRecently, a wide variety of real‐world problems are solved via cloud computing, deep learning, machine learning, artificial intelligence, and the Internet of Things (IoT). These methodologies are concerned with different areas like smart cities, agriculture, transportation systems, and healthcare systems. The existing researchers focused on the health care monitoring application along with IoT and cloud computing. It met several shortcomings in case of computational complexity, cost, time, and improper health care data storage and so forth. To overcome these challenges, the novel IoT‐enabled secure healthcare monitoring model is proposed in this study. At first, several sensors are deployed in the human body thereby collecting patients' data with respect to vital parameters like body temperature deviation. The patient's health record dataset consists of 10 attributes namely phone numbers, marital status, address, age, name, heartbeat rate, oxygen level, smoking, temperature, and blood pressure. The data size reduction and normalization are performed in the IoT medical sensor dataset which contains the redundant or irrelevant attributes that are eliminated during pre‐processing. The artificial hummingbird (AHB) algorithm‐based convolutional neural network (AHB‐CNN) model performs both feature extraction and classification of cancer disease. The AHB‐CNN model classifies whether the patient is prone to cancer or not based on the sensor input collected. The received results are then sent to the hospital management for analysis. The Rivest‐Shamir‐Adleman (RSA) encryption method is mostly used in this study due to the major benefits it gives in terms of asymmetric encryption, ease of use, simpler deployment, and high security associated with factoring large prime numbers. A modified RSA algorithm is used in this article which uses the double encryption‐decryption process and “n” prime numbers to enhance the security of the conventional RSA algorithm. The data is always encrypted during transit before being stored in the cloud or for further processing. Depending upon the experimental consequences, the proposed method established superior performances compared to other state‐of‐art techniques.

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people's lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

Solving problems of clustering and classification of cancer diseases based on DNA methylation data

The article deals with the problem of diagnosis of oncological diseases based on the analysis of DNA methylation data using algorithms of cluster analysis and supervised learning. The groups of genes are identified, methylation patterns of which significantly change when cancer appears. High accuracy is achieved in classification of patients impacted by different cancer types and in identification if the cell taken from a certain tissue is aberrant or normal. With method of cluster analysis two cancer types are highlighted for which the hypothesis was confirmed stating that among the people affected by certain cancer types there are groups with principally different methylation pattern.