Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer.

Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.

Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis.

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.

Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder.

Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug-drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.