Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.publishedVersio

Algorithms Implemented for Cancer Gene Searching and Classifications

Understanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is con-ducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification

Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA tool

ORIGINAL ARTICLES Epidemiology Biostatistics and Public Health - 2020, Volume 17, Number 2Investigation of diagnostic value of artificial intelligence systems in the diagnosis of breast cancer based on histopathological images using Meta-MUMS DTA toolInvestigation of diagnostic value of artificialintelligence systems in the diagnosis of breastcancer based on histopathological imagesusing Meta-MUMS DTA toolABSTRACTBackground: Various artificial intelligence systems are available for diagnosing breast cancer based onhistopathological images. Assessing the performance of existing methodologies for breast cancer diagnosis is vital.Methods: The SCOPUS database has been searched for studies up to December 15, 2018. We extracted the data,including "true positive," "true negative," "false positive," and "false negative". The pooled sensitivity, pooled specificity,positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve of summary receiveroperating characteristic curve were useful in assessing the diagnostic accuracy. Egger's test, Deeks' funnel plot, SVE(Smoothed Variance regression model based on Egger’s test), SVT (Smoothed Variance regression model based onThompson’s method), and trim and fill methodologies were essential tests for publication bias identification.Results: Three studies with eight approaches from thirty-seven articles were found eligible for further analysis. Asensitivity of 0.95, a specificity of 0.78, a PLR of 7525, an NLR of 0.06, a DOR of 88.15, and an AUC of 0.953showed high significant heterogeneity; however, the reason was not the threshold effect. The publication bias wasdetected by SVE, SVT, and trim and fill analysis.Conclusion: The artificial intelligent (AI) systems play a pivotal role in the diagnosis of breast cancer usinghistopathological cell images and are important decision-makers for pathologists. The analyses revealed that theoverall accuracy of AI systems is promising for breast cancer; however, the pooled specificity is lower than pooledsensitivity. Moreover, the approval of the results awaits conducting randomized clinical trials with sufficient dat

Ants constructing rule-based classifiers.

Classifiers; Data; Data mining; Studies;

En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report.  Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%

Kecerdasan Buatan dalam Teknologi Kedokteran: Survey Paper

Dalam makalah ini, akan diberikan gambaran mengenai penerapan kecerdasan buatan dalam bidang medis, khususnya untuk pembuatan keputusan serta pengklasifikasian dalam ilmu diagnostik berdasarkan gambar biomedis. Beberapa teknologi kecerdasan buatan (AI) terbukti mampu melakukan optimasi klasifikasi gambar biomedis. Studi ini mengumpulkan studi representatif yang menunjukan bagaimana AI digunakan untuk memecahkan masalah pada ilmu diagnostik. Ini juga mengakui metode kecerdasan buatan yang sering digunakan dalam memecahkan masalah pada ilmu diagnostik, seperti metode jaringan syaraf tiruan, support vector machine, pohon keputusan, serta metode particle swarm optimization. Masalah-masalah dalam ilmu diagnostik yang dapat terpecahkan menggunakan metode tersebut diantaranya yaitu analisis tumor otak MRI dan kanker payudara. Berdasarkan hasil survei yang penulis lakukan, untuk metode yang paling efektif dan efisien dalam melakukan diagnosis pada bidang medis adalah metode CNN hanya saja metode CNN membutuhkan data yang cukup besar untuk melakukan klasifikasi

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence