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Simple Summary: Laryngeal cancer poses a major global health burden, with late-stage diagnoses contributing to decreased survival rates. Recently, deep learning and deep convolutional neural network models have exhibited significant attention in the diagnosis of various diseases like skin cancer and diabetic retinopathy. Therefore, this study focuses on the design and development of a deep learning-based laryngeal cancer detection and classification model. The proposed model exploited a hyperparameter-tuned EfficientNetB0 model with a multi-head bidirectional gated recurrent unit for classification. In addition, the Dwarf Mongoose Optimization algorithm is applied for the hyperparameter tuning process. The experimental results stated that the proposed model is found to be an accurate and reliable approach for automated detection of laryngeal cancer. Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures. [ABSTRACT FROM AUTHOR]

In 2019, the novel coronavirus disease 2019 (COVID-19) ravaged the world. As of July 2021, there are about 192 million infected people worldwide and 4.1365 million deaths. At present, the new coronavirus is still spreading and circulating in many places around the world, especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic again. The symptoms of COVID-19 are diverse, and most patients have mild symptoms, with fever, dry cough, and fatigue as the main manifestations, and about 15.7% to 32.0% of patients will develop severe symptoms. Patients are screened in hospitals or primary care clinics as the initial step in the therapy for COVID-19. Although transcription-polymerase chain reaction (PCR) tests are still the primary method for making the final diagnosis, in hospitals today, the election protocol is based on medical imaging because it is quick and easy to use, which enables doctors to diagnose illnesses and their effects more quickly3. According to this approach, individuals who are thought to have COVID-19 first undergo an X-ray session and then, if further information is required, a CT-scan session. This methodology has led to a significant increase in the use of computed tomography scans (CT scans) and X-ray pictures in the clinic as substitute diagnostic methods for identifying COVID-19. To provide a significant collection of various datasets and methods used to diagnose COVID-19, this paper provides a comparative study of various state-of-the-art methods. The impact of medical imaging techniques on COVID-19 is also discussed. [ABSTRACT FROM AUTHOR]

Forecasting consists basically of using data to predict the value of the attributes to promote micro- and macro-level decision making. There are many methods to do prediction extending from complexity and data requirement. In this paper, we present the method of an autoregressive integrated moving average (ARIMA), multilayer perceptron artificial neural network (ANN) model and decision tree (DT) method to forecast time-series data, also we use different methods to measure the accuracy of the forecasting of the patient dying after having Ebola virus in the Republic of Liberia over the period of 25 March 2014 to 13 April 2016. The data source is from World Health Organization (WHO). [ABSTRACT FROM AUTHOR]

Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.