The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of two-dimensional (2D) mammography images. Thirty mammogram images with known benign tumors were obtained from The Cancer Imaging Archive (TCIA) datasets and were randomly selected as subjects. The samples were enhanced for semiautomatic segmentation sets using the Active Contour Model in MATLAB 2019a before analysis by two independent observers. Meanwhile, the images without any enhancement were segmented manually. The samples were divided into three categories: (1) CLAHE images, (2) AHE images, and (3) manual segmented images. Radiomics features were extracted using algorithms provided by MATLAB 2019a software and were assessed with a reliable intra-class correlation coefficient (ICC) score. Radiomics features for the CLAHE group (ICC = 0.890 ± 0.554, p < 0.05) had the highest reproducibility compared to the features extracted from the AHE group (ICC =0.850 ± 0.933, p < 0.05) and manual delineation (ICC = 0.673 ± 0.807, p > 0.05). Features in all three categories were more robust for the CLAHE compared to the AHE and manual groups. This study shows the existence in variation for the radiomics features extracted from tumor region that are segmented using various image enhancement techniques. Semiautomatic segmentation with image enhancement using CLAHE algorithm gave the best result and was a better alternative than manual delineation as the first two techniques yielded reproducible descriptors. This method should be applicable for predicting outcomes in patient with breast cancer.

Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network—multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method’s performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.

Recently, Artificial Intelligence (AI) has been considered as a valuable tool to detect early infections and to monitor the condition of the infected patients. Machine learning and deep learning is a subset of AI that uses neural network algorithms. Hence, this study aimed to explore the sensitivity of CoV-19 detection by using CAD4COVID program (Delft Imaging, Netherland), and to evaluate the accuracy of the classifier performance using Automated Machine Learning (Auto ML) algorithm. 70 chest X-ray (CXR) images were assessed and of that, 39, 20 and 11 patients receive low range (0-35), medium range (36-65) and high range (66-100) probability score, respectively. The sensitivity of AutoML detection was 0.99, with an accuracy of 0.83. In summary, the AutoML with the best optimizer may comparable to CAD4COVID in detection of Cov-19 in term of its accuracy and sensitivity.

Radioecology is considered as a significant field in environmental radioactivity that observe the impact of natural or man-made radiation to human population. The awareness towards risk from the radioactivity of the former mining area on human health is always lacking and often underestimated. Hence, this study aimed to evaluate the radioactivity from the residential area in Klang Valley which was a former mining area and to determine its associated risk. 20 soil samples have been collected systematically and analyzed by using the High Purity Germanium (HPGe) gamma spectrometer (Canberra, Australia). The 226Ra, 232Th and 40K were predetermined from the soil samples and were ranged from 11.91-54.09 Bq/kg, 8.95-49.50 Bq/kg and 974.64 Bq/kg, respectively. The risk of radiation hazard to human being were analyzed and categorized based on studied area. As per analyzed the radium equivalent activity, radiation hazard index, external hazard index and total air absorbed dose rate, the value were 305.90 ± 111.84 Bq/kg, 2.25 ± 0.85, 0.30 ± 0.84 and 139.5 ± 49.42 nGy/h, respectively. These values were compared with the recommendation by United Nations Scientific Committee on the Effect of Atomic Radiation (UNSCEAR) international standard safe limit. Locations at point P2, P6, P16 and P17 were observed to have highest potential risk and has a radioactive element that can endanger the health of the surrounding population on external exposure via external exposure and ingestion from the food chain. Overall, the mean annual effective dose from the external exposure received by an individual was estimated to be 0.17±0.01 mSv/y, which is far below than the annual dose limit of 1 mSv/y of the public.