Learning Convolutional Neural Network Model Research Articles

Using Deep Learning to Detect Tooth Decay in Children-To what extent can a convolutional neural network be used to detect tooth decay in images of children’s teeth accurately?

Approximately 530 million children have untreated cavities, which could affect future permanent teeth if not treated well. Researchers have enhanced X-ray detection of tooth decay to improve the detection of cavities. However, dentophobia and lack of insurance and dentist availability are barriers that constrain thousands from receiving proper dental care. This study used a deep learning convolutional neural network model to address this problem to detect cavities. Three hundred twenty-two photos taken by a camera of child patients were used. The dataset was classified into two classes: cavity and no cavity. The MobileNetV2 architecture was used for feature extraction and cavity detection. The model was then trained and tested so that it classified photos into two categories. The model achieved an accuracy of 93.5%.

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).

An Automatic Non-Destructive External and Internal Quality Evaluation of Mango Fruits based on Color and X-ray Imaging with Machine Learning and Deep Learning Based Classification Models

Quality evaluation of food products, agricultural produce to be specific, has gained momentum from past few decades due to the increased awareness among consumers across the world. This has resulted in the increased emphasis on the development and use of quality assessment techniques in food industry. Moreover, there is a need to automate the quality monitoring of agricultural produce like fruits and vegetables which is otherwise done manually in developing countries hence labor intensive, time consuming and subjective in nature. This paper presents an empirical analysis to build a rapid, robust, real-time, non-destructive computer vision based quality assessment model for mango fruits. The work employs the automatic disease classification of mango fruits based on machine and deep learning models. Firstly, the dataset of colored mango fruits images with 2279 images falling into three classes and another dataset of soft X-ray images of mango fruits with 572 images belonging to two quality classes are developed for detecting external and internal defects, respectively. The multilayer perceptron neural network (MLP NN) with two hidden layers, which may be considered as the starting point for deep learning technique, is proposed as machine learning model to classify the color images of mango fruits into one of three external quality classes with 95.1% accuracy and also to classify the soft X-ray images into two internal quality classes with 97.5% accuracy. In order to step out of feature engineering, actual deep learning convolutional neural network (CNN) models, a customized CNN model and pre-trained CNN models, VGGNet (VGG16) and DenseNet121 were also explored for mango disease classification. The maximum validation accuracy of custom CNN was found to be with 91.52% and 98.7% for color and augmented X-ray images, respectively. The classification accuracy of pre-trained models were found to be reasonably good for the color images but exhibited high variability in results and made it difficult to draw a general conclusion for the proposed datasets. However, the proposed MLP NN model based on few basic intensity and geometric features and also the proposed customized CNN model were found to be the best models and they outperform the state of the art reported in the literature.

Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications

A Brain–Computer Interface (BCI), integrated with the Internet of Medical Things (IoMT) and based on electroencephalogram (EEG) technology, allows users to control external devices by decoding brainwave patterns. Advanced deep learning-based BCIs, especially those utilizing sensorimotor rhythms (SMRs), have emerged as direct brain–device communication facilitators. SMRs involve users imagining limb motions to induce specific brain activity changes in the motor cortex. Despite progress, some users struggle with BCIs due to weak signals, individual variability, and limited task applicability. This study introduces an unsupervised EEG preprocessing pipeline for SMR-based BCIs. It evaluates an EEG dataset recorded during finger movements, employing two cleaning methods: an investigator-dependent pipeline and our proposed unsupervised method. Two distinct feature datasets are generated: one from cleaned EEG data processed into spectrogram images using supervised preprocessing, and another from data cleaned using our proposed unsupervised pipeline. The study extensively assesses five transfer learning convolutional neural network (TL-CNN) models for distinguishing Motor Imagery (MI) from finger movements (Mex) using the generated datasets. A novel probability fusion technique is developed to enhance TL-CNN classification in Mex versus MI finger-pinching actions. Comparative results show that the fusion-based method outperforms other state-of-the-art methods when applied to unsupervised EEG data. Specifically, our proposed approach achieves 97.9% accuracy, 93.4% precision, 95% recall, and an F1-score of 93.2%, demonstrating significant progress in distinguishing MI and Mex activities through the use of our unsupervised pre-processing pipeline and fusion-based CNN method. Our findings demonstrate the potential of our approach to serve as a benchmark for the global interdisciplinary research community and enable the development of future more effective and user-friendly real-time BCI systems.

A cost-effective CNN-BEM coupling framework for design optimization of horizontal axis tidal turbine blades

The paper proposes a novel cost-effective framework that combines deep learning convolutional neural network (CNN) and blade element momentum (BEM) models for optimizing the performance of three-dimensional (3D) horizontal axis tidal turbines (HATTs). The framework employs signed distance function (SDF) to reconstruct the three-dimensional blade geometry, utilizes CNN to identify the hydrodynamic performance of each blade section, and ultimately predicts the performance of HATT using BEM. On top of the new CNN-BEM model, the rotor blade geometrical optimization by multi-objective non-dominated sorting genetic algorithm (NSGA-II) is carried out to obtain a better trade-off solution with the maximal power coefficient of turbine and minimal hydrodynamic load exerted on the blades. The results show that the CNN-BEM model has good agreement with experimental data and reduces prediction time by 46.7% compared to the conventional Xfoil-BEM model, while reducing general optimization time by 20.1%. The new model's cost-efficiency allows for a better trade-off solution with reduced hydrodynamic load while maintaining the power coefficient. Thus, the proposed model has the capability to deliver both accurate and fast prediction and optimization of HATT performance, making it a valuable tool for guiding the design of tidal turbines.

Spatial attention-based residual network for human burn identification and classification

Diagnosing burns in humans has become critical, as early identification can save lives. The manual process of burn diagnosis is time-consuming and complex, even for experienced doctors. Machine learning (ML) and deep convolutional neural network (CNN) models have emerged as the standard for medical image diagnosis. The ML-based approach typically requires handcrafted features for training, which may result in suboptimal performance. Conversely, DL-based methods automatically extract features, but designing a robust model is challenging. Additionally, shallow DL methods lack long-range feature dependency, decreasing efficiency in various applications. We implemented several deep CNN models, ResNeXt, VGG16, and AlexNet, for human burn diagnosis. The results obtained from these models were found to be less reliable since shallow deep CNN models need improved attention modules to preserve the feature dependencies. Therefore, in the proposed study, the feature map is divided into several categories, and the channel dependencies between any two channel mappings within a given class are highlighted. A spatial attention map is built by considering the links between features and their locations. Our attention-based model BuRnGANeXt50 kernel and convolutional layers are also optimized for human burn diagnosis. The earlier study classified the burn based on depth of graft and non-graft. We first classified the burn based on the degree. Subsequently, it is classified into graft and non-graft. Furthermore, the proposed model performance is evaluated on Burns_BIP_US_database. The sensitivity of the BuRnGANeXt50 is 97.22% and 99.14%, respectively, for classifying burns based on degree and depth. This model may be used for quick screening of burn patients and can be executed in the cloud or on a local machine. The code of the proposed method can be accessed at https://github.com/dhirujis02/Journal.git for the sake of reproducibility.

Deep learning-based characterization of the drug tolerant persister cell state in lung cancer.

141 Background: Lung cancer is the leading cause of cancer-related lethality globally. Targeted therapies improve the clinical outcome of cancer treatment; however, a subpopulation of cancer cells survive during initial therapy and evolve into drug tolerant persister cells (DTPs) that maintain a residual disease reservoir. Residual disease preludes acquired resistance and tumor progression; therefore, identifying and eliminating DTPs could benefit future treatment paradigms. We have shown that Hippo pathway effector YAP1 (Yes Associated Protein-1) is activated in oncogene-driven lung cancers when cancer cells are exposed to various targeted therapies, such as EGFR, ALK, and RAS inhibitors. YAP1 transcriptional activation during targeted therapy is characterized by its increased nuclear localization and interaction with transcription factors. This activation promotes the expression of genes involved in cell survival, cellular plasticity, and metabolic reprogramming at the residual disease state. We hypothesized that detection of intrinsic or acquired persister cells may aid the development of optimized treatment regimens. In this study, we are building image-based deep learning models to identify YAP1 activation-mediated DTPs from histologically stained slides. Methods: H&E or YAP-stained immunohistochemistry (IHC) images from clinical lung cancer and patient-derived tumor xenograft samples were collected throughout targeted therapy and were annotated with semi-automation using high performance computing clusters. A deep learning model (U-Net algorithm) was used for image segmentation, training, validation, and testing. Results: 1638 images were annotated and over 80,000 patches from these images for YAP positive cells (or regions of interest) comprised the training dataset with semi-supervised automation. Subsequently, we built a customized deep-learning model to detect YAP-mediated DTP cell states from whole histopathological image slides. The deep learning-based model achieved excellent accuracy of 0.8238 and 0.9091 in training, and 0.8040 and 0.8949 in validation datasets for two different annotations, respectively. For a test dataset, the model obtained 0.81 and 0.902 accuracy for the two annotations, respectively. Conclusions: We have constructed a deep learning convolutional neural network model to infer the presence of the YAP1 activation-mediated drug tolerant persister cell state prior to or during targeted treatment of lung cancer. Implementing our AI-based model into routine lung cancer care in the future could identify patient subpopulations with YAP1 activated tumors who would most benefit from receiving YAP1-targeted small molecule inhibitors.

Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network

Lung cancer is one of the major illnesses that contribute to millions of fatalities worldwide. Numerous deaths could be saved through the early identification and categorization of lung cancers. However, with traditional approaches, classification accuracy cannot be produced. To detect and classify lung diseases, a deep learning convolutional neural network model has been developed. LDDC, the customized local trilateral filter, is used for pre-processing the lung images from computing tomography for non-local trilateral filters. The region of interest for lung cancer was successfully restricted throughout the segmentation of the disease using hybrid fuzzy morphological procedures. To extract the deep seismic features, the Laplacian pyramid decomposition method was utilized for the segmented image. This paper covers an overall analysis of non-local trilateral filter Processing, hybrid fuzzy morphological techniques and analysis of patient and disease characteristics of LIDR- IDRI and FDA data of Group A (no co-AGA), P-value, Multi-mut Patient, Group B (with a co-AGA).

Design and development of electronic stethoscope for early screening of valvular heart disease prediction

The significant human population of the world is suffering from valvular heart diseases and dies due to the lack of a simple predictive diagnosis system. Identifying the abnormalities in heart sound needs excellent auscultation skills and experience. Thus an electronic stethoscope is designed and developed using different microphonic heads. In this research work, hardware development of an electronic stethoscope interfaced with raspberry pi 4B and software development of the proposed CNN-based deep learning model is carried out. The Bluetooth-enabled electronic stethoscope is used to auscultate the heart sound analyzed with a developed deep learning Convolutional Neural Network-based Efficient Network model. The developed CNN model is designed with classifiers to predict valvular diseases accurately. The analysis result is almost in real-time processed and stored in the Cloud. The design provides a better way of studying PCG signal analysis, which eventually reduces the cost and makes the system compact. The proposed model has been trained through a standard and validated heart sound bank of normal and abnormal diseases and predicts the abnormality with accuracy. The proposed modified Efficient Net - B3 model scored an accuracy of 99.35 ± 0.34% on the test dataset with a sensitivity of 98.84 ± 0.07% and specificity of 98.23 ± 0.52%. The Selenium Web Driver tool with Google Drive API is used to automate the web application PCG signal analysis. Finally, the SQLite database has been used as a back-end server to store the patient record. The system is low-cost and portable, with data remotely accessible and tested with volunteers. The developed system can be used in rural areas where there is a lack of medical facilities exists and can be used to initiate primary screening of valvular diseases.

Object detection and activity recognition in video surveillance using neural networks

PurposeThis paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.Design/methodology/approachThe goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.FindingsThe results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.Originality/valueThis paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

The Study and Application of Facial Recognition Models

Facial recognition technology is a biometric application that uniquely identifies or authenticates a person by comparing and analyzing facial features based on the width of a person's face. Face recognition technology has gained popularity recently thanks to the development and invention of artificial intelligence and other technologies. The traditional methods of face recognition technology development—the method based on geometric features and the method based on 3D models—as well as the deep learning Convolutional Neural Network (CNN) model VGG-Face for face recognition—are analyzed, compared, and described in this paper. It also provides a detailed description of face recognition technology. In this work, accuracy and TAR values are employed for geometric feature matching by comparing the metric results of each method on a dataset for better comparison. Our results show that while both traditional geometric feature modeling methods and 3D modeling methods can achieve good accuracy, however, they do not perform as well as humans in terms of accuracy. The Vgg-Face based on the deep learning model completely exceeds the 3D model method in terms of accuracy and TAR value, and is even more accurate than humans.

Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.

A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber

Recently, deep learning methods have become one of the hottest topics in predicting material properties, however, one bottleneck in current research is the simultaneous analysis of heterogeneous data. In this study, a deep learning fusion model is developed for the first time to predict the material properties of carbon fiber monofilament using textual (macroscopic properties of composites and matrix) and visual (two-point statistics of microstructures) data. For this, 1200 stochastic microstructures are generated using the greedy-based generation (GBG) algorithm. Then, the statistical representations of microstructures are determined using two-point statistics and the macroscopic properties are calculated based on a micro-scale finite element (FE) simulation. Finally, the visual and textual data are fed into the convolutional neural network (CNN) and multi-layer perceptron (MLP) fusion model for predicting the mechanical properties of carbon fibers. The developed hybrid CNN-MLP fusion model achieves encouraging average testing R2 of longitudinal modulus, transverse modulus, in-plane shear modulus, major Poisson’s ratio, and out-of-plane shear modulus of carbon fibers with values of 0.991, 0.969, 0.984, 0.903, and 0.955, respectively. Thus, the proposed strategy provides a promising framework for predicting material properties via multisource heterogeneous data and is expected to accelerate the smart design and optimization of materials.

Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems

The Human Activity Recognition (HAR) problem leverages pattern recognition to classify physical human activities as they are captured by several sensor modalities. Remote monitoring of an individual’s activities has gained importance due to the reduction in travel and physical activities during the pandemic. Research on HAR enables one person to either remotely monitor or recognize another person’s activity via the ubiquitous mobile device or by using sensor-based Internet of Things (IoT). Our proposed work focuses on the accurate classification of daily human activities from both accelerometer and gyroscope sensor data after converting into spectrogram images. The feature extraction process follows by leveraging the pre-trained weights of two popular and efficient transfer learning convolutional neural network models. Finally, a wrapper-based feature selection method has been employed for selecting the optimal feature subset that both reduces the training time and improves the final classification performance. The proposed HAR model has been tested on the three benchmark datasets namely, HARTH, KU-HAR and HuGaDB and has achieved 88.89%, 97.97% and 93.82% respectively on these datasets. It is to be noted that the proposed HAR model achieves an improvement of about 21%, 20% and 6% in the overall classification accuracies while utilizing only 52%, 45% and 60% of the original feature set for HuGaDB, KU-HAR and HARTH datasets respectively. This proves the effectiveness of our proposed wrapper-based feature selection HAR methodology.

Personality Prediction from Handwriting using Fine-tuned Transfer Learning Models

Experts in computational linguistics have done a number of research to identify and categorize personality characteristics at various aspects, including terms, sentences, paragraphs, and recommendations. In this research work five stages model was proposed and the experimental result was evaluated on handwriting images.This paper presents a comparative analysis of fine-tuning transfer learning convolutional neural network models such as VGG16, ResNet50 and GoogleNet for personality detection. The results of fine-tuned models are assessed using the accuracy, precision, recall and f1_score measure. From the results it was observed that ResNet50 have achieved best accuracy as compared to GoogleNet and VGG16

Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet

Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible–near-infrared–shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements.

Fault States Diagnosis of Marine Diesel Engine Valve Based on a Modified VGG16 Transfer Learning Method

The marine diesel engine is an important power machine for ships. Traditional machine learning methods for diesel engine fault diagnosis usually require a large amount of labeled training data, and the diagnosis performance may decline when encounters vibrational and environmental interference. A transfer learning convolutional neural network model based on VGG16 is introduced for diesel engine valve leakage fault diagnosis. The acquired diesel engine cylinder head vibration signal is first converted to time domain, frequency domain, and wavelet decomposition images. Secondly, the VGG16 deep convolutional neural network is pretrained using the ImageNet dataset. Subsequently, fine tuning the network based on the pretrained basic parameters and image enhancement methods. Finally, the well‐trained model is adopted to train and test the target dataset. In addition, the cosine annealing learning rate setting method is used to make the learning rate close to the global optimal solution. Experimental results show that the proposed method has higher accuracy and better robustness against noise with a small sample dataset than traditional methods and deep learning models. This study not only demonstrates a novel view for the diagnosis of marine diesel engine valve leakage, but also provides an applicable diagnosis method for other similar issues.

Automated uniform recognition to enhance video surveillance at correctional services in South Africa

Injuries from inmate altercations are common in correctional service facilities. Monitoring incidents manually from video surveillance can be challenging. Computer vision has the potential to assist security personnel in securing facilities. This work compares two methods for recognising occupational uniforms with the aim of improving situational awareness and safety in prisons. The first method uses histograms of hue and saturation (HS) colour-space features and a shallow learning classifier. The second method uses convolutional neural network (CNN) models trained with either hand-engineered or automatically learned features. A training dataset with civilians, South African correctional service and police uniforms was created. The experimental results demonstrate comparable performance from shallow learning algorithms and CNN models. Machine learning algorithms evaluated on the proposed colour features achieved an average balanced performance (F1-score) of 0.85 and inference times range from 0.01 to 4.9 milliseconds.

Early CTU Termination and Three-steps Mode Decision Method for Fast Versatile Video Coding

Versatile Video Coding (VVC) has been recently becoming popular in coding videos due to its compression efficiency. To reach this performance, Joint Video Experts Team (JVET) has introduced a number of improvement techniques to VVC coding model. Among them, VVC Intra coding introduces a new concept of quad-tree nested multi-type tree (QTMT) and extends the predicted modes with up to 67 options. As a result, the complexity of the VVC Intra encoding also greatly increases. To make VVC Intra coding more feasible in real-time applications, we propose in this paper a novel deep learning based fast QTMT and an early mode prediction method. At the first stage, we use a learned convolutional neural network (CNN) model to predict the coding unit map and then fed into the VVC encoder to early terminate the block partitioning process. After that, we design a statistical model to predict a list of most probable modes (MPM) for each selected Coding using (CU) size. Finally, we employ a so-called three-steps mode decision algorithm to estimate the optimal directional mode without sacrificing the compression performance. The proposed early CU splitting and fast intra prediction are integrated into the latest VTM reference software. Experimental results show that the proposed method can save 50.2% of encoding time with a negligible BD-Rate increase.

Skin lesion classification of dermoscopic images using machine learning and convolutional neural network

Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.