The diagnostics of osteoarthritis : A fine-tuned transfer learning approach

Osteoarthritis (OA) is an illness that causes the wear of the protective cartilage between two bones in joints. Patients with OA disease suffer from pain in joints, stiffness, loss of flexibility, amongst others. Conventional means of identifying OA is considered laborious and prone to mistakes. Owing to the advancement of computer vision and computational models, automatic diagnostics is possible. Therefore, this paper proposes the use of transfer learning models for the classification of the different classes of OA. The pre-trained Convolutional Neural Network models used are VGG16, VGG19 and Resnet50, with their fully connected layers, are heuristically fine-tuned. It was demonstrated from this preliminary study that the fine-tuned VGG16 model could classify the classes fairly well in comparison to those that have been reported in the literature

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis

Deep learning in Cancer Diagnostics: a feature-based transfer learning evaluation

This book highlights the use of state-of-the-art Deep learning (DL) techniques in cancer diagnosis. It includes the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin. This book also discusses the use of DL methods in combination with imaging techniques to identify cancer correctly

A VGG16 feature-based transfer learning evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC)

Oral Squamous Cell Carcinoma (OSCC) is the most prevalent type of oral cancer. Early detection of such cancer could increase a patient’s survival rate by 83%. This chapter shall explore the use of a feature-based transfer learning model, i.e., VGG16 coupled with different types of conventional machine learning models, viz. Support Vector Machine (SVM), Random Forest as well as k-Nearest Neighbour (kNN) as a means to identify OSCC. A total of 990 evenly distributed normal and OSCC histopathological images are split into the 60:20:20 ratio for training, testing and validation, respectively. A testing accuracy of 93% was recorded via the VGG16- RF pipeline from the study. Consequently, the proposed architecture is suitable to be deployed as artificial intelligence-driven computer-aided diagnostics and, in turn, facilitate clinicians for the identification of OSCC

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time

The classification of skateboarding tricks via transfer learning pipelines

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with gridsearched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWTMobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks executio

Classifying motion intention from EMG signal: A kNN approach

The use of robotic systems has been investigated over the past couple of decades in improving rehabilitation training of hemiplegic patients. In an ideal situation, the system should be able to detect the intention of the subject and assist them as needed in performing certain training tasks. In this study, we leverage on the information from the electromyogram (EMG) signals, to detect the subject’s intentions in generating motion commands for a robotic assisted upper limb rehabilitation system. As EMG signals are known for its very low amplitude apart from its susceptibility to noise, hence, signal processing is mandatory, and this step is non-trivial for feature extraction. The EMG signals are recorded from ten healthy subjects’ bicep muscles, who are required to provide a voluntary movement of the elbow’s flexion and extension along the sagittal plane. The signals are filtered by a fifth-order Butterworth filter. Several features were extracted from the filtered signals namely waveform length, mean absolute value, root mean square and standard deviation. Two different classifiers viz. Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) were investigated on its efficacy in accurately classifying the pre-intention and intention classes based on the selected features, and it was observed from this investigation that the kNN classifier yielded a better classification with a classification accuracy of 96.4

Forecasting road deaths in Malaysia using support vector machine

An average of 6,350 people died every year in Malaysia due to road traffic accidents. A published data of Malaysian road deaths in 20 years since 1997 reveals that the number of fatalities has not really declined with a difference of less than 10% from one year to the next. Forecasting the number of fatalities is beneficial in planning a counter measure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as auto regressive integrated moving average (ARIMA). The model was used in the previous Road Safety Plan of Malaysia to set a target death toll to be reduced in 2020, albeit being inaccurate. This study proposes a new approach in forecasting the road deaths, by means of a machine learning algorithm known as Support Vector Machine. The length of various types of road, number of registered vehicles and population were among the eight features used to develop the model. Comparison between the actual road deaths and the prediction demonstrates a good agreement, with a mean absolute percentage error of 2% and an R-squared value of 85%. The Linear kernel-based Support Vector Machine was found to be able to predict the road deaths in Malaysia with reasonable accuracy. The developed model could be used by relevant stakeholders in devising appropriate poli-cies and regulations to reduce road fatalities in Malaysia

Characterization on conduction properties of carboxymethyl cellulose/kappa carrageenan blend-based polymer electrolyte system

The present work deals with the development of carboxymethyl cellulose (CMC) blended with kappa carrageenan (KC) as a host-based polymer electrolyte (PE) system. The CMC/KC films were successfully prepared using solution casting method and were characterized through electrical impedance spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and X-ray diffraction (XRD) methods, respectively. The FTIR spectrum revealed that the significant region of interaction transpires at wave number 1,057, 1,326, 1,584, and 3,387 cm−1 which correspond to the bending of C–O–C, bending of –OH, asymmetric of –COO− as well as the stretching of –OH, respectively. It has also been demonstrated that the complexation process occurred between CMC and KC. The CMC/KC blend PE system with a ratio of 80:20 achieved an optimum conductivity of 3.91 × 10−7 S cm−1 and had the lowest crystallinity percentage as suggested by the XRD analysis

A support vector machine approach in predicting road traffic mortality in Malaysia

Traffic mortality rate is the baseline through which road safety plans of a country could be evaluated. A reliable and reasonable analysis of road traffic related injuries and their leading causes is vital to the road safety investigation, evaluation as well as policymaking. Malaysia has the third highest fatality rate from road traffic accidents in Asia as well as in South East Asia. Although many researchers have attempted to provide predictive models of road traffic mortality (RTM) in the country, the predictions are found to be rather unsatisfactory in forecasting the causes as well as the future road fatality. It is hypothesized that the inability of the previous models to provide a good prediction of the RTM might be attributed to the complicated and non-linear data relationship of the underlying causes of road traffic accidents. A Support Vector Machine (SVM) is demonstrated to be effective in solving both classifications as well as regression problems owing to its efficacy to cater for the non-linear relationship of a dataset. The present investigation proposed the application of SVM based model variations namely; Linear, Quadratic, Cubic, Fine, Medium as well as Coarse Gaussian-based SVM in predicting the RTM. A dataset from 1972 to 1994 was obtained from the Malaysian road traffic database. The data were trained on the SVM model variations. It was demonstrated that the Linear based SVM model is able to provide a reasonable prediction of the RTM with only 12% error. It is, therefore, inferred that a reasonable prediction of RTM in Malaysia could be achieved through the employment of non-conventional statistical techniques