Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation

The accurate segmentation of retinal vessels is a challenging taskdue to the presence of various pathologies as well as the low-contrast ofthin vessels and non-uniform illumination. In recent years, encoder-decodernetworks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we proposea lightweight convolutional neural network (CNN)-based encoder-decoderdeep learning model for accurate retinal vessels segmentation. The proposeddeep learning model consists of encoder-decoder architecture along withbottleneck layers that consist of depth-wise squeezing, followed by fullconvolution, and finally depth-wise stretching. The inspiration for the proposed model is taken from the recently developed Anam-Net model, whichwas tested on CT images for COVID-19 identification. For our lightweightmodel, we used a stack of two 3 × 3 convolution layers (without spatialpooling in between) instead of a single 3 × 3 convolution layer as proposedin Anam-Net to increase the receptive field and to reduce the trainableparameters. The proposed method includes fewer filters in all convolutionallayers than the original Anam-Net and does not have an increasing numberof filters for decreasing resolution. These modifications do not compromiseon the segmentation accuracy, but they do make the architecture significantlylighter in terms of the number of trainable parameters and computation time.The proposed architecture has comparatively fewer parameters (1.01M) thanAnam-Net (4.47M), U-Net (31.05M), SegNet (29.50M), and most of the otherrecent works. The proposed model does not require any problem-specificpre- or post-processing, nor does it rely on handcrafted features. In addition,the attribute of being efficient in terms of segmentation accuracy as well aslightweight makes the proposed method a suitable candidate to be used in thescreening platforms at the point of care. We evaluated our proposed modelon open-access datasets namely, DRIVE, STARE, and CHASE_DB. Theexperimental results show that the proposed model outperforms several stateof-the-art methods, such as U-Net and its variants, fully convolutional network (FCN), SegNet, CCNet, ResWNet, residual connection-based encoderdecoder network (RCED-Net), and scale-space approx. network (SSANet) in terms of {dice coefficient, sensitivity (SN), accuracy (ACC), and the areaunder the ROC curve (AUC)} with the scores of {0.8184, 0.8561, 0.9669, and0.9868} on the DRIVE dataset, the scores of {0.8233, 0.8581, 0.9726, and0.9901} on the STARE dataset, and the scores of {0.8138, 0.8604, 0.9752,and 0.9906} on the CHASE_DB dataset. Additionally, we perform crosstraining experiments on the DRIVE and STARE datasets. The result of thisexperiment indicates the generalization ability and robustness of the proposedmodel.

A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

Melanoma,due to its higher mortality rate,is considered as one of the most pernicious types of skin cancers,mostly affecting the white populations.It has been reported a number of times and is now widely accepted,that early detection of melanoma increases the chances of the subject’s survival.Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques.In thiswork,we propose a framework that accurately segments,and later classifies,the lesion using improved image segmentation and fusion methods.The proposed technique takes an image and passes it through two methods simultaneously;one is the weighted visual saliency-based method,and the second is improved HDCT based saliency estimation.The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region.The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model-trained by applying transfer learning.The simulation results show improved performance compared to several existing methods.

A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting characteristics.Nevertheless,existing NDN faces three significant challenges,including security,privacy,and routing.In particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious content.For instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road accidents.In such a situation,trust in the content-providing vehicles brings a new challenge.On the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another challenge.Moreover,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN.In this connection,our contribution is threefold.Unlike existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes.Based on ML evaluation results,vehicles accept or discard served content.Secondly,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ledger.Finally,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)approach.We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive Bayes.The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

Gastric Tract Disease Recognition Using Optimized Deep Learning Features

Artificial intelligence aids for healthcare have received a great deal of attention.Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy(WCE).Early diagnosis facilitates appropriate treatment and saves lives.Deep learning-based techniques have been used to identify gastrointestinal ulcers,bleeding sites,and polyps.However,small lesions may be misclassified.We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images.Initially,we use hybrid contrast enhancement to distinguish diseased from normal regions.Then,a pretrained model is fine-tuned,and further training is done via transfer learning.Deep features are extracted from the last two layers and fused using a vector length-based approach.We improve the genetic algorithm using a fitness function and kurtosis to select optimal features that are graded by a classifier.We evaluate a database containing 24,000 WCE images of ulcers,bleeding sites,polyps,and healthy tissue.The cubic support vector machine classifier was optimal;the average accuracy was 99%.

An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

Information and communication technologies are spreading rapidly due to their fast proliferation in many fields.The number of Internet users has led to a spike in cyber-attack incidents.E-commerce applications,such as online banking,marketing,trading,and other online businesses,play an integral role in our lives.Network Intrusion Detection System(NIDS)is essential to protect the network from unauthorized access and against other cyber-attacks.The existing NIDS systems are based on the Backward Oracle Matching(BOM)algorithm,which minimizes the false alarm rate and causes of high packet drop ratio.This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations.To address the existing system issues,this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance.The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets.The experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17%detection rate and a 0.22%lower false alarm rate than the existing solution.