V3 Models Research Articles

Impact of Pre-trained Weights on MobileNet Architectures for Animal Classification

This study aims to address the problem of improving animal image classification accuracy using different versions of MobileNet. Accurate animal classification plays a vital role in biodiversity protection, environmental monitoring, and agriculture. The research is significant because existing studies focus on specific models and datasets, leaving a gap in the comparative performance analysis of MobileNet versions. To address this issue, MobileNet V1, V2, and V3 models were utilized, both with and without ImageNet pre-trained weights. The models were trained on a dataset composed of 30,179 images from two sources, covering 13 animal categories. The experiment involved training the models over 10 epochs using a standard configuration of the TensorFlow framework, with accuracy serving as the primary evaluation metric.The results showed that MobileNet V3Large, with pre-trained weights, achieved the highest accuracy (97.43%), outperforming V1 and V2. Using pre-trained weights consistently enhanced performance, as models without pre-training exhibited lower accuracy and slower convergence. This study contributes by providing a comprehensive comparison of MobileNet versions in animal classification tasks, demonstrating the importance of pre-training and model architecture optimization for achieving high accuracy in image classification.

SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma

Background and objectiveMalignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors. MethodsIn this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance. ResultsSeveral measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %. ConclusionsThe research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.

Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high surface. By taking 4 categories of motion movements in exercise therapy, data were taken using normal child subjects as standard movements, which then became a reference for CP child therapy. The limitations of therapy in physical care prompted researchers to investigate the use of image processing as input to Human Computer Interaction (HCI) in the process of motion detection-based therapy. In research using Deep learning as a classifier, namely using the CNN Model (Inception V3, Resnet152, and VGG16 architectural models). The results obtained by the CNN (Inception V3) model have the best performance with an accuracy percentage of 98%.

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

An accumulation of aberrant cells called a brain tumor is the outcome of unregulated cell division. Brain cancers can be found using magnetic resonance imaging (MRI). The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and have high performance. This dearth of training data makes it critical to explore methods such as data augmentation. In this sense, data augmentation methods are widely used in strategies to train networks, and with small data sets being vital in medicine due to the limited access to data, this work aims to identify the best classification system by considering the prediction accuracy in this vein. Data augmentation is performed on the database and fed into the three convolutional neural network (CNN) models. A comparison line is drawn between the three models based on accuracy and performance on the Inception v3 models, Mobile Net V2, and Squeeze Net network for brain tumor detection and classifying 350 brain MR images. The statistical methods were modified in order to evaluate these algorithms. With 0.992% accuracy, 0.993% recall, 0.989% precision, and 0.994% F1 score, the Squeeze Net model performed the best. The Mobile Net V2 model, which had an accuracy of 0.964%, came next. When the research's findings were compared to those of related studies in the literature, they revealed better success rates than those of the majority of investigations.

Diabetic Retinopathy Stage Detection Using CNN and Inception V3

The project explores the deployment of Convolutional Neural Networks (CNN) and the Inception V3 model for the automated detection and classification of diabetic retinopathy stages using fundus images. Recognizing diabetic retinopathy as a leading cause of blindness among the working-age population globally, this research aims to streamline the diagnostic process, traditionally reliant on the manual examination by ophthalmologists. Through the utilization of the DRIVE and STARE datasets, the project benchmarks the performance of CNN and Inception V3 models in accurately categorizing the severity of diabetic retinopathy into five distinct stages. The comparison between these models is grounded on parameters such as accuracy, loss, and predicted value, with findings indicating Inception V3's superiority in both performance metrics and diagnostic precision. This advancement could significantly contribute to early and more accessible detection of diabetic retinopathy, thereby mitigating progression towards blindness. Furthermore, the project underscores the potential of deep learning algorithms in enhancing diagnostic methodologies for retinal diseases, paving the way for future explorations in the field of medical imaging and artificial intelligence.

Developing a Pain Identification Model Using a Deep Learning Technique

In this study, the authors proposed a pain identification model using facial expressions. An image extraction technique was developed using the liquid neural network to extract diverse images from the video files. The authors used the DenseNet 201 and MobileNet V3 models to build a hybrid feature engineering technique. They applied quantization aware training to improve the efficiency of the models. The Prkachin and Solomon Pain Intensity score was used for the image classification. They fine-tuned the LightGBM model using the random search algorithm for identifying pain from the facial images. The authors used the Denver Intensity of Spontaneous Facial Action dataset to generalize the proposed model. The performance evaluation outlined the significant performance of the proposed model in identifying pain using the images. In addition, it demands limited computational resources to identify pain. Healthcare and rehabilitation centers can implement the proposed model to provide adequate services to disabled individuals.

A Fine-Tuned CatBoost-Based Speech Disorder Detection Model

The classification of speech disorders (SDs) is crucial for treating children with speech impairment (SI). An automated SD classification can assist speech therapists in rendering services to children with SI in rural areas. Automated techniques for detecting SDs provide objective assessments of speech attributes, including articulation, fluency, and prosody. Clinical examinations and quantitative assessments provide an in-depth understanding of the patient’s speaking abilities and limitations. Existing deep learning (DL) models for SD detection often lack generalization across diverse populations and speech variations, leading to suboptimal performance when applied to individuals with different linguistic backgrounds or dialects. This study introduces a DL-based model for classifying normal and abnormal speeches using voice samples. To overcome the overfitting and bias, the authors construct convolutional neural network models with the weights of MobileNet V3 and EfficientNet B7 models for feature extraction (FE). To improve performance, they integrate the squeeze and excitation block with the MobileNet V3-based FE model. Similarly, the EfficientNet B7-model-based FE is improved using the structure pruning technique. The enhanced CatBoost model differentiates the normal and abnormal speeches using the extracted features. The experimental analysis is performed using the public dataset that contains 4620 utterances of healthy children and 2178 utterances of children with SI. The comparative study reveals the exceptional performance of the proposed SD classification model. The model outperforms the current SD classification models. It can be employed in clinical settings to support speech therapists. Substantial training with diverse voice samples can improve the generalizability of the proposed model.

Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning

A rapid and accurate yield estimation is of great significance to the management and sustainable development of Camellia Oleifera forests. Consequently, the simultaneous and accurate detection of key phenology features of Camellia Oleifera (buds, flowers, fruits) is crucial for precise yield estimation. This not only enables robotic harvesting but also allows for the prediction of peak flowering and fruit ripening periods to determine the optimal harvesting time. However, in recent studies, only Camellia Oleifera fruits have been marked and detected. Therefore, to enable rapid yield estimation, it is necessary to simultaneously detect the key phenology stages (buds, flowers, fruits) of Camellia Oleifera. In this study, we annotated, trained, and predicted Camellia Oleifera buds, flowers, and fruits collected via field cameras to observe their daily quantitative changes. Quantity change curves were generated to estimate crucial phenology stages. Phenology feature detection and transfer learning were performed using the YOLO v5 model, widely used YOLO v3 model, and CenterNet model with center point prediction, all trained on the same dataset. The best model for phenology feature detection was selected based on a comparison of average precision, with the YOLO v5 model achieving a higher mean Average Precision (mAP) value of 91.31 % compared to the CenterNet (85.43 %) and YOLO v3 (81.00 %) models. In YOLO v5, the AP values for bud, flower, and fruit detection were 82.80 %, 98.13 %, and 92.99 %, respectively, surpassing the CenterNet model by 3.97 %, 2.44 %, and 11.23 %, and the YOLO v3 model by 6.39 %, 17.13 %, and 11.67 %. The image size was adapted from 4000 × 3000 pixels to 512 × 512 pixels for model optimization. Additionally, data from the Seedling Center of Liuyang City collected at different years and times were utilized to showcase the generalizability and scalability of the selected models, resulting in mAP values of 86.14 %, 80.17 %, and 69.20 % for the three above-mentioned models respectively. The plotted phenology change curves unveiled that Camellia Oleifera undergoes four stages: fruit enlargement period, bud enlargement period, flowering period, and flower wilting period. The conclusion can be drawn that using field cameras and YOLOv5 can simultaneously achieve real-time detection of key phenology features (buds, flowers, and fruits) of Camellia Oleifera, in order to further record crucial phenology patterns (such as flowering peaks and fruit ripening periods). This study offers theoretical references and scientific evidence for monitoring changes in key phenology features of Camellia Oleifera.

An ensemble approach for classification of tympanic membrane conditions using soft voting classifier

Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of 880 otoscopy images, including different eardrum cases such as normal, earwax plug, myringosclerosis, and chronic otitis media. The prediction results of these models were evaluated with voting approaches to increase the overall prediction accuracy. In this context, the performances of both soft and hard voting ensembles were examined. Soft voting ensemble framework achieved highest performance in experiments with 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. Our proposed model achieved the highest classification performance so far in the current dataset. The results reveal that our voting ensemble-based DL approach showed quite high performance for the diagnosis of middle ear disease. In clinical applications, this approach can provide a preliminary diagnosis of the patient's condition just before field experts make a diagnosis on otoscopic images. Thus, our proposed approach can help field experts to diagnose the disease quickly and accurately. In this way, clinicians can make the final diagnosis by integrating automatic diagnostic prediction with their experience.

Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images

Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and complexities in interpreting results pose challenges to healthcare centers in implementing deep learning (DL)-based CAD detection models. Ensemble learning (EL) allows developers to build an effective CAD detection model by integrating the outcomes of multiple medical imaging models. In this study, the authors build an EL-based CAD detection model to identify CAD from coronary computer tomography angiography (CCTA) images. They employ a feature engineering technique, including MobileNet V3, CatBoost, and LightGBM models. A random forest (RF) classifier is used to ensemble the outcomes of the CatBoost and LightGBM models. The authors generalize the model using two benchmark datasets. The proposed model achieved an accuracy of 99.7% and 99.6% with limited computational resources. The generalization results highlight the importance of the proposed model’s efficiency in identifying CAD from the CCTA images. Healthcare centers and cardiologists can benefit from the proposed model to identify CAD in the initial stages. The proposed feature engineering can be extended using a liquid neural network model to reduce computational resources.

OPG-based dental age estimation using a data-technical exploration of deep learning techniques.

Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes.

Early stage black pepper leaf disease prediction based on transfer learning using ConvNets

Plants get exposed to diseases, insects and fungus. This causes heavy damages to crop resulting in various leaves diseases. Leaf diseases can be diagnosed at an early stage with the aid of a smart computer vision system and timely disease prevention can be targeted. Black pepper is a medicinal plant that is extensively used in Ayurvedic medicine because of its therapeutic properties. The proposed work represents an intelligent transfer learning technique through state-of-the-art deep learning implementation using convolutional neural network to predict the presence of prominent diseases in black pepper leaves. The ImageNet dataset available online is used for training deep neural network. Later, this trained network is utilized for the prediction of the newly developed black pepper leaf image dataset. The developed data set consist of real time leaf images, which are candidly taken from the fields and annotated under supervision of an expert. The leaf diseases considered are anthracnose, slow wilt, early stage phytophthora, phytophthora and yellowing. The hyperparameters chosen for tuning in to deep learning models are initial learning rates, optimization algorithm, image batches, epochs, validation and training data, etc. The accuracy obtained with 0.001 learning rate ranges from 99.1 to 99.7% for the Inception V3, GoogleNet, SqueezeNet and Resnet18 models. Proposed Resnet18 model outperforms all model with 99.67% accuracy. The resulting validation accuracy obtained using these models is high and the validation loss is low. This work represents improvement in agriculture and a cutting edge deep neural network method for early stage leaf disease identification and prediction. This is an approach using a deep learning network to predict early stage black pepper leaf diseases.

A battle royale optimization with feature fusion-based automated fruit disease grading and classification

<abstract> <p>Fruit Disease Detection (FDD) using Computer Vision (CV) techniques is a powerful strategy to accomplish precision agriculture. Because, these techniques assist the farmers in identifying and treating the diseased fruits before it spreads to other plants, thus resulting in better crop yield and quality. Further, it also helps in reducing the usage of pesticides and other chemicals so that the farmers can streamline their efforts with high accuracy and avoid unwanted treatments. FDD and Deep Learning (DL)-based classification involve the deployment of Artificial Intelligence (AI), mainly the DL approach, to identify and classify different types of diseases that affect the fruit crops. The DL approach, especially the Convolutional Neural Network (CNN), has been trained to classify the fruit images as diseased or healthy, based on the presence or absence of the disease symptoms. In this background, the current study developed a new Battle Royale Optimization with a Feature Fusion Based Fruit Disease Grading and Classification (BROFF-FDGC) technique. In the presented BROFF-FDGC technique, the Bilateral Filtering (BF) approach is primarily employed for the noise removal process. Besides, a fusion of DL models, namely Inception v3, NASNet, and Xception models, is used for the feature extraction process with Bayesian Optimization (BO) algorithm as a hyperparameter optimizer. Moreover, the BROFF-FDGC technique employed the Stacked Sparse Autoencoder (SSAE) algorithm for fruit disease classification. Furthermore, the BRO technique is also employed for optimum hyperparameter tuning of the SSAE technique. The proposed BROFF-FDGC system was simulated extensively for validation using the test database and the outcomes established the enhanced performance of the proposed system. The obtained outcomes emphasize the superior performance of the BROFF-FDGC approach than the existing methodologies.</p> </abstract>

Multimodal Feature Fusion Using Optimal Transfer Learning Approach for Lung Cancer Detection and Classification on CT Images

Lung cancer detection is the process of detecting the presence of lung tumor or abnormalities in the lungs. Early diagnosis is crucial for increasing the chances of patient survival and successful treatment. When compared to X-rays, Computed Tomography (CT) images are more sensitive and are increasingly being used for the diagnosis and screening of lung tumors. They provide complete cross-sectional images of the lungs and it will even detect small lesions. AI and Machine learning (ML) approaches are most commonly employed to analyse medical images (e.g. CT scans) and detect lung cancer. This algorithm can help radiologists identify patterns indicative or subtle abnormalities of cancer. Medical diagnosis, particularly in complex diseases such as lung cancer, frequently involves ambiguity. The diagnostic system can alleviate ambiguity via cross-verifying findings from various sources by fusing multimodal features. Multimodal feature fusion using deep learning (DL) algorithm is an advanced technology that leverages the abilities of deep neural networks to combine data from three different modalities or sources for better robustness in several applications, namely natural language processing, image, and data analysis, etc. This study introduces a Multimodal Feature Fusion using an Optimal Transfer Learning Method for Lung Cancer Detection and Classification (MFFOTL-LCDC) methodology on CT images. The chief objective of the MFFOTL-LCDC methodology is to exploit the feature fusion process for the identification and classification of lung tumor. To attain this, the MFFOTL-LCDC model undergoes a multimodal feature fusion approach to derive feature vectors using 3 DL approaches such as SqueezeNet, CapsNet, and Inception v3 models. Besides, the MFFOTL-LCDC technique applies the remora optimization algorithm (ROA) for the hyperparameter choice of 3 DL models. For lung cancer recognition, the MFFOTL-LCDC algorithm exploits the deep extreme learning machine (DELM) algorithm. A series of simulations were conducted to ensure the greater lung cancer recognition outcomes of the MFFOTL-LCDC methodology. The extensive outcomes determine the improved results of the MFFOTL-LCDC technique over recent DL approaches.

RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.

EEG Brain Signal Processing for Epilepsy Detection

Background: Millions of neurons make up the human brain, and they play an important role in controlling the body's response to internal and external motor and sensory stimuli. These neurons can function as contact conduits between the human body and the brain. Analyzing brain signals or photographs will help one better understand cognitive function. These states are linked to a particular signal frequency that aids in the comprehension of how a complex brain system works. Objective: Electroencephalography (EEG) is a useful method for locating brain waves associated with different countries on the scalp. Epilepsy is a condition where the brain or some part of it is overactive and sends too many signals. This results in seizures causing muscles to twitch or whole-body convulsions. Methods: In this paper, the author has designed a model to predict epilepsy using machine learning algorithms and deep learning models. For the machine learning algorithm, different features were extracted and a particle swarm optimization algorithm was used to select the best feature which was classified using wavelet transform.Vgg16, Vgg19, and Inception V3 models are used for the detection of epilepsy. Results: The inception V3 model results in 97.87% accuracy which is better than all other techniques. 5.1% accuracy improvement has been observed using a machine learning algorithm. The model is compared using existing work and it has been observed that the proposed model results better. Conclusion: The technique for modeling EEG signals and insight brain signals recorded during surgical procedures has been identified in detail. 0.7% and 0.13% accuracy improvement were achieved when the model is validated on Kaggle and CHB-MIT datasets respectively.

Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques

Many prime grain-producing nations have implemented steps to limit export of grains as COVID-19 has expanded over the globe; food security has sparked significant worry from a number of stakeholders. One of the most crucial concerns facing all nations is how to increase grain output. However, the diseases occur in crops remain a challenge for countless farmers, therefore it is critical to understand their severity promptly and precisely to guide the them in taking additional measures to lessen the chances of plants being affected furthermore.
 This paper describes a deep learning model for the identification of crop diseases that can achieve high accuracy with low processing power. The model, called the inception v3 network, has been tested on a tomato leaf dataset and has obtained a average identification accuracy of 98.00% and further the ensemble of two inception v3 models with slight diversity achieved an accuracy of 98.11%. The results suggest that this model could be useful in improving food security by helping farmers quickly and accurately identify crop diseases and take appropriate measures to prevent further spread.

A concatenation approach-based disease prediction model for sustainable health care system

In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF).

Unlocking COVID-19 patterns: Exploring deep learning models for precise recognition and classification of CT images

Coronavirus is a pestilence sickness that truly affects old individuals and patients with persistent illnesses and causes life threatening diseases. Preventing the entire world from this epidemic, quick and accurate detection of COVID-19 plays a crucial role. To contain the advancement of Covid, it is important to build up a dependent and fast technique to recognize the individuals who are influenced and segregate them until full recovery is made. In this study, we introduce a groundbreaking deep CNN model, leveraging the latest advancements in the field, to accomplish precise categorization of COVID-19 CT images. We employ the publicly available HUST-19 dataset, encompassing an impressive collection of 13,980 CT scan images. By harnessing the power of this extensive dataset, our model aims to improve the precision and reliability of COVID-19 classification. In our research, we propose three innovative architectures for deep convolutional neural networks (CNNs), known as AlexNet, Inception V3 and VGG19 models, to get the accurate diagnosis of COVID-19 patients based on images obtained from a CT scan. In order to assess the effectiveness of CNN-based models in a quantitative manner, we employ several key metrics, including Accuracy, Precision, Recall and F-score. The findings of our study indicate that all these three architectures gave remarkable results but InceptionV3 came on the top with testing accuracy of 99.95% with precision, recall and f1-score of 1, 1, and 1 respectively. The results underscore the potential of our suggested method in accurately diagnosing COVID-19 cases utilizing CT scan images.

Detection of Parkinson’s disease based on spectrograms of voice recordings and Extreme Learning Machine random weight neural networks

Parkinson’s disease consists in the degeneration of the mesencephalic black substance, affecting the dopaminergic vias. Its causes are varied, including exposure to pesticides, genetic factors and, one of the most influential ones, age. Given the decrease in dopamine levels, the most common symptoms are the appearance of tremors and muscle rigidity. Due to the rigidity of the muscles, the patient has voice alterations which have great potential for non-invasive and early diagnosis of the disease. In addition, considering the low cost of the sound recorder respect to the clinical studies, this approach allows the diagnosis of Parkinson’s disease in a large number of people. Recent works, which present the analysis of voice recordings through Convolutional Neural Networks, show high level of accuracy in the diagnosis of Parkinson’s disease. Convolutional Neural Networks use a Multilayer Neural Network to classify convolutional feature vectors. In order to improve the training time of the classifier, in this paper the use Extreme Learning Machines are proposed. Experiments considering 4 types of spectrograms with AlexNet, VGG-16, SqueezeNet, Inception V3 and ResNet-50 Convolutional Neural Networks models. In the experiments, hit rate, training and testing time, sensitivity and the specificity indicators of all the neural architectures involved in the work are objectively compared. It is shown that the Extreme Learning Machine have a high level of accuracy in the diagnosis of Parkinson’s disease but with reduced training time.