Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.

Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. Recently, convolutional neural networks (CNNs) have been carried out for medical diagnosis to obtain higher accuracy than standard machine learning algorithms and specialists' expertise. Therefore, the proposed approach involves using the Bayesian hyperparameter optimization with the CNN architecture for automatic diagnosis of ear imagery database including four classes: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). The suggested approach was trained using 616 otoscopic images, and the performance of this approach was assessed using 264 testing images. In this paper, the performance of ear disease classification was compared in terms of accuracy, sensitivity, specificity, and positive predictive value (PPV). The results produced a classification accuracy of 98.10%, a sensitivity of 98.11%, a specificity of 99.36%, and a PPV of 98.10%. Finally, the suggested approach demonstrates how to locate optimal CNN hyperparameters for accurate diagnosis of ear diseases while taking time into account. As a result, the usefulness and dependability of the suggested approach will lead to the establishment of an automated tool for better categorization and prediction of different ear diseases.

Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.

The external and middle ear conditions are diagnosed using a digital otoscope. The clinical diagnosis of ear conditions is suffered from restricted accuracy due to the increased dependency on otolaryngologist expertise, patient complaint, blurring of the otoscopic images, and complexity of lesions definition. There is a high requirement for improved diagnosis algorithms based on otoscopic image processing. This paper presented an ear diagnosis approach based on a convolutional neural network (CNN) as feature extraction and long short-term memory (LSTM) as a classifier algorithm. However, the suggested LSTM model accuracy may be decreased by the omission of a hyperparameter tuning process. Therefore, Bayesian optimization is used for selecting the hyperparameters to improve the results of the LSTM network to obtain a good classification. This study is based on an ear imagery database that consists of four categories: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). This study used 880 otoscopic images divided into 792 training images and 88 testing images to evaluate the approach performance. In this paper, the evaluation metrics of ear condition classification are based on a percentage of accuracy, sensitivity, specificity, and positive predictive value (PPV). The findings yielded a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a PPV of 100% for the testing database. Finally, the proposed approach shows how to find the best hyperparameters concerning the Bayesian optimization for reliable diagnosis of ear conditions under the consideration of LSTM architecture. This approach demonstrates that CNN-LSTM has higher performance and lower training time than CNN, which has not been used in previous studies for classifying ear diseases. Consequently, the usefulness and reliability of the proposed approach will create an automatic tool for improving the classification and prediction of various ear pathologies.

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

A 3D image segmentation for lung cancer using V.Net architecture based deep convolutional networks.

Lung segmentation of chest CT scan is utilised to identify lung cancer and this step is also critical in other diagnostic pathways. Therefore, powerful algorithms to accomplish this accurate segmentation task are highly needed in the medical imaging domain, where the tumours are required to be segmented with the lung parenchyma. Also, the lung parenchyma needs to be detached from the tumour regions that are often confused with the lung tissue. Recently, lung semantic segmentation is more suitable to allocate each pixel in the image to a predefined class based on fully convolutional networks (FCNs). In this paper, CT cancer scans from the Task06_Lung database were applied to FCN that was inspired by V.Net architecture for efficiently selecting a region of interest (ROI) using the 3D segmentation. This lung database is segregated into 64 training images and 32 testing images. The proposed system is generalised by three steps including data preprocessing, data augmentation and neural network based on the V-Net model. Then, it was evaluated by dice score coefficient (DSC) to calculate the ratio of the segmented image and the ground truth image. This proposed system outperformed other previous schemes for 3D lung segmentation with an average DCS of 80% for ROI and 98% for surrounding lung tissues. Moreover, this system demonstrated that 3D views of lung tumours in CT images precisely carried tumour estimation and robust lung segmentation.