Convolutional Neural Networks For Detection Research Articles

Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias

PurposeFocal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognostic implications for postoperative seizure freedom. Despite advancements in imaging methods, FCD detection remains challenging. House et al. (2021) introduced a convolutional neural network (CNN) for automated FCD detection and segmentation, achieving a sensitivity of 77.8%. However, its clinical applicability was limited due to a low specificity of 5.5%. The objective of this study was to improve the CNN’s performance through data-driven training and algorithm optimization, followed by a prospective validation on daily-routine MRIs. Material and methodsA dataset of 300 3 T MRIs from daily clinical practice, including 3D T1 and FLAIR sequences, was prospectively compiled. The MRIs were visually evaluated by two neuroradiologists and underwent morphometric assessment by two epileptologists. The dataset included 30 FCD cases (11 female, mean age: 28.1 ± 10.1 years) and a control group of 150 normal cases (97 female, mean age: 32.8 ± 14.9 years), along with 120 non-FCD pathological cases (64 female, mean age: 38.4 ± 18.4 years). The dataset was divided into three subsets, each analyzed by the CNN. Subsequently, the CNN underwent a two-phase-training process, incorporating subset MRIs and expert-labeled FCD maps. This training employed both classical and continual learning techniques. The CNN’s performance was validated by comparing the baseline model with the trained models at two training levels. ResultsIn prospective validation, the best model trained using continual learning achieved a sensitivity of 90.0%, specificity of 70.0%, and accuracy of 72.0%, with an average of 0.41 false positive clusters detected per MRI. For FCD segmentation, an average Dice coefficient of 0.56 was attained. The model’s performance improved in each training phase while maintaining a high level of sensitivity. Continual learning outperformed classical learning in this regard. ConclusionsOur study presents a promising CNN for FCD detection and segmentation, exhibiting both high sensitivity and specificity. Furthermore, the model demonstrates continuous improvement with the inclusion of more clinical MRI data. We consider our CNN a valuable tool for automated, examiner-independent FCD detection in daily clinical practice, potentially addressing the underutilization of epilepsy surgery in drug-resistant focal epilepsy and thereby improving patient outcomes.

GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN

Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset.

Abstract 6173: The development and evaluation of a convolutional neural network for melanoma detection in whole slide images

Abstract The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of inter-observer variation. The application of convolutional neural networks (CNNs) to whole slide images (WSIs), may reveal new insights into tumor morphology and therefore patient prognosis. Melanoma morphology appears to be of greater significance than in other solid tumors, with Breslow thickness remaining the strongest prognostic indicator. Other biomarkers based on tumor morphology have been generated and although some have been found to be prognostically superior to Breslow thickness, none have been integrated into clinical workflows. This may in part be explained by their demands on pathologist time. Therefore, this work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, which may be used for prognostic biomarker generation. 1,157 WSIs containing cutaneous melanoma from five datasets (three from the University of Leeds, one from the Melanoma Institute Australia, as well as The Cancer Genome Atlas) have been used in the initial development and evaluation of the CNN. A custom-designed 2-class tumor segmentation network with a fully convolutional architecture was trained using annotations. The CNN was evaluated using various methodologies, including comparison at per-pixel and per-tumor levels as compared to manual annotation, as well as variation across 3 scanning platforms (Leica Aperio AT2 (Milton Keynes, UK), Roche Ventana DP600 (Arizona, US) and Hamamatsu NanoZoomer S360 (Hamamatsu City, Japan). The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9% respectively across the 5 test sets. There were no statistical differences between tumor dimensions generated by the CNN as compared to manual annotation. Similarly, there were no statistically significant differences between CNN generated tumor dimensions across three scanning platforms. We have developed and performed initial evaluation of a CNN which appears to accurately detect invasive cutaneous melanoma of no specific type in WSIs for objective evaluation of tumor morphology. Future work should interrogate these data further for its propensity to predict survival outcomes. Citation Format: Emily L. Clarke, Derek Magee, Julia Newton-Bishop, William Merchant, Marlous Hall, Robert Insall, Nigel Maher, Richard Scolyer, Grace Farnworth, Anisah Ali, Sally O'Shea, Darren Treanor. The development and evaluation of a convolutional neural network for melanoma detection in whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6173.

Development of AI Models from Mammography Images with CNN for Early Detection of Breast Cancer

Early detection of breast cancer with computer assistance has developed since two decades ago. Artificial intelligence using the convolutional neural network (CNN) method has successfully predicted mammography images with a high level of accuracy similar to human brain learning. The potential of AI models provides opportunities to spot breast cancer cases better. This research aims to develop AI models with CNN using the public DDSM dataset with a sample size of 1871, consisting of 1546 images for training and 325 images for testing. These AI models provided prediction results with different accuracy rate. Increasing the accuracy of the AI model can be done by improving the image quality before the modeling process, increasing the number of datasets, or carrying out a more profound iteration process so that the AI model with CNN can have a better level of accuracy.

Decoding scheme based on CNN for differential free space optical communication system

A high-speed and robust decoding algorithm based on Convolutional Neural Networks (CNN) is proposed for differential free space optical communication system. The system adopts bipolar complementary pulse width modulation (BCPWM) that can enhance the amplitude of the signal and avoid interference from common mode signals in a single channel system. In addition, BCPWM has amplitude and envelope characteristics. The deep learning-based detector proposed by the signal does not require channel state information. It directly feeds the received signal into a deep neural network, and CNN can effectively extract amplitude and envelope features from the signal. The experimental comparison was conducted with bipolar non-return to zero (BNRZ) signals with only amplitude features to verify the demodulation ability of CNN for signals with different features. With a bit error rate of 10−5, the required signal-to-noise ratio for BNRZ is 13 dB, and the required signal-to-noise ratio for BCPWM is 8 dB. The demodulation performance of CNN, maximum likelihood (ML), and Volterra equalizer detector were compared. For BCPWM signals, compared to ML and Volterra equalizer detectors, the proposed CNN-based detector reduces the required signal-to-noise ratio by 2 dB and 8 dB at a bit error rate of 10−5, respectively.

Best performance with fewest resources: Unveiling the most resource-efficient Convolutional Neural Network for P300 detection with the aid of Explainable AI

Convolutional Neural Networks (CNNs) have shown remarkable prowess in detecting P300, an Event-Related Potential (ERP) crucial in Brain–Computer Interfaces (BCIs). Researchers persistently seek simple and efficient CNNs for P300 detection, exemplified by models like DeepConvNet, EEGNet, and SepConv1D. Noteworthy progress has been made, manifesting in reducing parameters from millions to hundreds while sustaining state-of-the-art performance. However, achieving further simplification or performance improvement beyond SepConv1D appears challenging due to inherent oversimplification. This study explores landmark CNNs and P300 data with the aid of Explainable AI, proposing a simpler yet superior-performing CNN architecture which incorporates (1) precise separable convolution for feature extraction of P300 data, (2) adaptive activation function tailored for P300 data, and (3) customized large learning rate schedules for training P300 data. Termed the Minimalist CNN for P300 detection (P300MCNN), this novel model is characterized by its requirement of the fewest filters and epochs to date, concurrently achieving best performance in cross-subject P300 detection. P300MCNN not only introduces groundbreaking concepts for CNN architectures in P300 detection but also showcases the importance of Explainable AI in demystifying the “black box” design of CNNs.

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal

One of the epileptic patients’ challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

A novel deep learning-based perspective for tooth numbering and caries detection.

The aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms. The dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process. According to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively. The proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently. CNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.

Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes

Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.

A Vision Enhancement and Feature Fusion Multiscale Detection Network

In the field of object detection, there is often a high level of occlusion in real scenes, which can very easily interfere with the accuracy of the detector. Currently, most detectors use a convolutional neural network (CNN) as a backbone network, but the robustness of CNNs for detection under cover is poor, and the absence of object pixels makes conventional convolution ineffective in extracting features, leading to a decrease in detection accuracy. To address these two problems, we propose VFN (A Vision Enhancement and Feature Fusion Multiscale Detection Network), which first builds a multiscale backbone network using different stages of the Swin Transformer, and then utilizes a vision enhancement module using dilated convolution to enhance the vision of feature points at different scales and address the problem of missing pixels. Finally, the feature guidance module enables features at each scale to be enhanced by fusing with each other. The total accuracy demonstrated by VFN on both the PASCAL VOC dataset and the CrowdHuman dataset is better than that of other methods, and its ability to find occluded objects is also better, demonstrating the effectiveness of our method.The code is available at https://github.com/qcw666/vfn.

Detecting phishing websites through improving convolutional neural networks with Self-Attention mechanism

Emerging technologies have made internet connection a vital activity facilitating access to many services. However, internet connection raises many security concerns, such as illegally acquiring private information, passwords, and identifiers. Phishing websites are the first choice for attackers that try to have users' private space. Social engineering attacks are performed by designing fake websites similar to real ones and inviting the victim to access those websites to collect their sensitive information and then redirect them to the actual site. Due to the importance of detecting phishing websites, building a robust detector that filters them and blocks their activity on the Internet is necessary. In this paper, we proposed a phishing website detector based on improving the convolutional neural network (CNN) with a self-attention mechanism. The proposed detector collects phishing Uniform Resource Locators (URLs) by treating them as strings. CNN models have proved their efficiency when dealing with text strings compared to Long Short-Term Memory (LSTM) which focuses on temporal features. Using CNN allows learning comprehensive features of the URLs and facilitates the detection of phishing ones. The self-attention mechanism was added to enhance the model's focus and detection accuracy. Besides, the training dataset was balanced by generating phishing URLs using a Generative Adversarial Network (GAN). A set of experiments has proved the robustness of the proposed detector by achieving high detection accuracy on the test set. Besides, the proposed detector was tested using unknown URLs and achieved excellent results. The improved CNN's detection precision of 99.7 is higher than the regular CNN model by 2.74%. The reported results show that using the self-attention mechanism has improved the detection accuracy and made the CNN model more efficient for detecting phishing websites.

Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy.

Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

Retracted: Dense Convolutional Neural Network for Detection of Cancer from CT Images.

[This retracts the article DOI: 10.1155/2022/1293548.].

Interpretability Analysis of Convolutional Neural Networks for Crack Detection

Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque process of training and operating CNNs, if the learned features for identifying cracks in the network are not evaluated, it may lead to safety risks. In this study, to evaluate the recognition basis of different crack detection networks; several crack detection CNNs are trained using the same training conditions. Afterwards, several crack images are used to construct a dataset, which are used to interpret and analyze the trained networks and obtain the learned features for identifying cracks. Additionally, a crack identification performance criterion based on interpretability analysis is proposed. Finally, a training framework is introduced based on the issues reflected in the interpretability analysis.

A Transfer Learning-Based Deep Convolutional Neural Network for Detection of Fusarium Wilt in Banana Crops

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

AbstractSoftware defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN‐integrated VANET (SDN‐int‐VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual‐based temporal attention red fox‐convolutional neural network (RTARF‐CNN) for detecting DDoS attacks in SDN‐int‐VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre‐processed data with the help of stacked contractive autoencoders (St‐CAE), which reduces the processing time of the introduced method. The selected features are classified by residual‐based temporal attention‐convolutional neural network (RTA‐CNN). The weight parameter of RTA‐CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF‐CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches.

A Survey of CNN-Based Approaches for Crack Detection in Solar PV Modules: Current Trends and Future Directions

Detection of cracks in solar photovoltaic (PV) modules is crucial for optimal performance and long-term reliability. The development of convolutional neural networks (CNNs) has significantly improved crack detection, offering improved accuracy and efficiency over traditional methods. This paper presents a comprehensive review and comparative analysis of CNN-based approaches for crack detection in solar PV modules. The review discusses various CNN architectures, including custom-designed networks and pre-trained models, as well as data-augmentation techniques and ensemble learning methods. Additionally, challenges related to limited dataset sizes, generalizability across different solar panels, interpretability of CNN models, and real-time detection are discussed. The review also identifies opportunities for future research, such as the need for larger and more diverse datasets, model interpretability, and optimized computational speed. Overall, this paper serves as a valuable resource for researchers and practitioners interested in using CNNs for crack detection in solar PV modules.

Robust spacecraft relative pose estimation via CNN-aided line segments detection in monocular images

Autonomous spacecraft relative navigation via monocular images became a hot topic in the past few years and, recently, received a further push thanks to the constantly growing field of artificial neural networks and the publication of several spaceborne image datasets. Despite the proliferation of spacecraft relative-state initialization algorithms developed, most architectures adopt computationally expensive solutions relying on convolutional neural networks (CNNs) that provide accurate output at the cost of a high computational burden that seems unfeasible for current spaceborne hardware. The paper addresses this issue by proposing a novel pose initialization algorithm based on lightweight CNNs. Inspired by previous state-of-the-art algorithms, the developed architecture leverages a fast and accurate target detection CNN followed by a line segment detection CNN capable of running with low inference time on mobile devices. The line segments and their junctions are grouped into complex geometrical groups, reducing the solution search space, and subsequently, they are adopted to extract the final pose estimate. As a main outcome, the analyses demonstrate that the lightweight architecture developed scores high accuracy in the pose estimation task, with a mean estimation error of less than 10 cm in translation and 2.5°in rotation. The baseline algorithm scores a mean SLAB error of 0.04552 with a standard deviation of 0.22972 in the test dataset. Detailed analyses demonstrate that the uncertainties on the overall pose score are driven mainly by errors in the relative attitude, which gives the highest contribution to the pose error metric adopted. The analyses on the error distributions point out that the uncertainties on the estimated relative position are higher in the camera boresight axis direction. Concerning the relative attitude, the algorithm proposed has higher uncertainties in estimating directions of the target x and y axes due to ambiguities related to the target geometry. Notably, the target detection CNN trained in this work outperforms the previous top scores in the benchmark dataset. The performances of the proposed algorithm have been investigated further by analyzing the effects on the accuracy due to the relative distance and the presence of background in the images. Lastly, the paper delves into the possibility of adopting a sub-portion of the 2D-to-3D match matrix made by the most complex perceptual groups identified that positively affects the overall run-time, pointing out the performances in terms of accuracy of the estimates and providing a comparison of both the baseline and the reduced match matrix versions against state-of-the-art algorithms concerning relative position and attitude errors and solution availability, highlighting the high accuracy and solution availability of the proposed architectures.

Automatic caries detection in bitewing radiographs: part I-deep learning.

The aim of this work was to assemble alarge annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance. A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the [Formula: see text] score and average precision (AP) with various thresholds of the intersection over union (IoU). The twelve tested architectures had [Formula: see text] scores of 0.72-0.76. Their performance was improved by ensembling which increased the [Formula: see text] score to 0.79-0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, [Formula: see text], and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88). The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset. Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.

DDOS ATTACK DETECTION USING HYBRID (CCN AND LSTM) ML MODEL

LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks) are two types of deep learning algorithms; by combining the strengths of LSTM and CNN, researchers have developed deep learning models that can effectively detect SDN (Software-Defined Network) attacks including Distributed Denial of Service. These models effectively analyze network traffic, encompassing temporal and spatial characteristics, resulting in precise identification of malicious traffic.In this research, a hybrid model composed of CNN and LSTM is used to detect the DDoS attack in SDN network. Where the CNN component of the model can identify spatial patterns in network traffic, such as the characteristics of individual packets, while the LSTM component can capture temporal patterns in traffic over time, such as the timing and frequency of traffic bursts. The proposed model has been trained on a labeled network traffic dataset, with one class representing normal traffic and another class representing DDoS attack traffic. During the training process, the model adjusts its weights and biases to minimize the difference between its predicted output and the actual output for each input sample. Once trained, the hybrid model classifies incoming network traffic in the dataset as either normal or malicious with an initial accuracy of (78.18%) and losses of (39.77%) at the 1st epoch till it reaches an accuracy of (99.99%) with losses of (9.29×10-5) at the epoch number 500. It should be mentioned that the hybrid model of CNN and LSTM for DDoS detection is implemented using Python Anaconda platform with an ETA 28ms/step.