Max-pooling Layers Research Articles

A pooling convolution model for multi-classification of ECG and PCG signals

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are physiological signals generated throughout the cardiac cycle. The application of deep learning techniques to recognize ECG and PCG signals can greatly enhance the efficiency of cardiovascular disease detection. Therefore, we propose a series of straightforward and effective pooling convolutional models for the multi-classification of ECG and PCG signals. Initially, these signals undergo preprocessing. Subsequently, we design various structural blocks, including a stacked block (MCM) comprising convolutional layer and max-pooling layers, along with its variations, as well as a residual block (REC). By adjusting the number of structural blocks, these models can handle ECG and PCG data with different sampling rates. In the final tests, the models utilizing the MCM structural block achieved accuracies of 98.70 and 92.58% on the ECG and PCG fusion datasets, respectively. These accuracies surpass those of all networks utilizing its variations. Moreover, compared to the models employing the REC structural block, the accuracies are improved by 0.02 and 4.30%, respectively. Furthermore, this research has been validated through tests conducted on multiple ECG and PCG datasets, along with comparisons to other published literature. To further validate the generalizability of the model, an additional experiment involving the classification of a synchronized ECG-PCG dataset was conducted. This dataset is divided into seven different levels of fatigue based on the amount of exercise performed by each healthy subject during the testing process. The results indicate that the model using the MCM block also achieved the highest accuracy.

Detection of ADHD Disorder in Children Using Layer-Wise Relevance Propagation and Convolutional Neural Network: An EEG Analysis

Purpose: Attention-Deficit-Hyperactivity-Disorder (ADHD) is a neurodevelopmental disorder that begins in early childhood and often persists into adulthood, causing personality issues and social behavior problems. Thus, detecting ADHD in its early stages and developing an effective therapy is of tremendous interest. This study presents a deep learning-based model for ADHD diagnosis in children. Materials and Methods: The 'First-National-EEG-Data-Analysis-Competition-with-Clinical-Application' dataset is used for this purpose. Following preprocessing, data is segmented into 3-second epochs, and frequency features are extracted from these epochs. The Fourier transform is applied to each channel separately, and the resulting two-dimensional matrix (channel×frequency) for each epoch is used as the Convolutional Neural Network's (CNN) input. The CNN is made up of two convolutional layers, two max pooling layers and two fully connected layers as well as the output layer (a total of 9 layers) for classification. To improve the method's performance, the output of the classification of each input variable is analyzed. In other words, the role of each channel/frequency in the final classification is being investigated using the Layer-wise Relevance Propagation (LRP) algorithm. Results: According to the results of the LRP algorithm, only efficient channels are employed as Convolutional Neural Network (CNN) inputs in the following stage. This method yields a final accuracy of 94.52% for validation data. In this study, the feature space is visualized, useful channels are selected, and deep structure capabilities are exploited to diagnose ADHD disorder. Conclusion: The findings suggest that the proposed technique can be used to effectively diagnose ADHD in children.

A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography

Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine interaction. Recently, there has been a growing interest in ER based on electroencephalogram (EEG) signals. However, due to the noisy, nonlinear, and nonstationary properties of electroencephalography signals, developing an automatic and high-accuracy ER system is still a challenging task. In this study, a pretrained deep residual convolutional neural network model, including 17 convolutional layers and one fully connected layer with transfer learning technique in combination frequency-channel matrices (FCM) of two-dimensional data based on Welch power spectral density estimate from the one-dimensional EEG data has been proposed for improving the ER by automatically learning the underlying intrinsic features of multi-channel EEG data. The experiment result shows a mean accuracy of 93.61 ± 0.84%, a mean precision of 94.70 ± 0.60%, a mean sensitivity of 95.13 ± 1.02%, a mean specificity of 91.04 ± 1.02%, and a mean F1-score of 94.91 ± 0.68%, respectively using 5-fold cross-validation on the DEAP dataset. Meanwhile, to better explore and understand how the proposed model works, we noted that the ranking of clustering effect of FCM for the same category by employing the t-distributed stochastic neighbor embedding strategy is: softmax layer activation is the best, the middle convolutional layer activation is the second, and the early max pooling layer activation is the worst. These findings confirm the promising potential of combining deep learning approaches with transfer learning techniques and FCM for effective ER tasks.

Developing a Quality Control System in a Continuous Hot Air Heating Process in Surimi Seafood Processing Using Image Analysis and Artificial Intelligence

In this study, the feasibility of classifying surimi gels during a continuous heating process using an artificial intelligence (AI) algorithm on labeled images was investigated. Surimi paste with varying corn starch concentrations (0%, 5%, and 10%) and moisture content levels (78% and 80%) from Alaska pollock were analyzed for the subtle physical changes. Rheological characterization and K-means clustering analysis performed on entire images captured from different batches of heated surimi gel indicated a four-stage transformation from its initial state to gel formation with the temperature ranges spanning 25 to <40 °C, 40 to <50 °C, 50 to <55 °C, and 55 to 80 °C. Subsequently, a Convolutional Neural Network (CNN) model based on the temperature classification was designed to interpret and classify these images. A total of 1000 to 1200 images were used for the training, testing, and validation purposes in the ratio 7:1:2. The CNN architecture incorporated essential elements including an input layer, convolutional layers, rectified linear unit (ReLU) activation functions, normalization layers, and max-pooling layers. The CNN model achieved validation accuracy >92.67% for individual mixture composition, 94.53% for classifying surimi samples based on moisture content, and gelation level, and 89.73% for complex classifications involving moisture content, starch concentration, and gelation stages. Additionally, it exhibited high average precision, recall, and F1 scores (>0.92), indicating precision and sensitivity in identifying relevant instances. The success of CNN in non-destructively classifying surimi gels with different moisture and starch contents is demonstrated in this work.

MFAGNet: multi-scale frequency attention gating network for land cover classification

ABSTRACT As a classic problem of high-resolution remote sensing image tasks, land cover classification has many challenges, for instance, variable scales and low-edge discrimination. Traditional spatial semantic segmentation methods based on single-scale feature extraction are challenging to model multi-scale features. They cannot effectively fit high-resolution remote sensing images with variable scales and are prone to misclassification issues. On the other hand, spatial multi-scale semantic segmentation methods lack edge constraints, which can easily lead to edge segmentation ambiguity issues. We propose a segmentation network based on a multi-scale frequency-domain attention gating mechanism (MFAGNet) to solve the above problems. Specifically, we obtain multi-scale features through multi-scale input and design the discrete cosine transform channel attention module (DCTCAM). In DCTCAM, we extract global low-frequency features and effectively alleviate misclassification caused by scale changes. The edge features of the image are obtained through the high-frequency feature extraction module (HFEM) by using edge detection operators to enhance the model’s ability to recognize edges. Additionally, no further pooling operations are employed except for a single max pooling layer in the backbone network. In this paper, we design a new gating mechanism to dynamically control the extraction ratio of various frequency features so that it can adapt to input images of different scales. This paper conducts comparative experiments on three high-resolution land cover datasets: GID, Vaihingen, and Potsdam. The new algorithm proposed compares with classic semantic segmentation models such as DANet, PSPNet, SegNet, UNet, DeepLabv3+, FcaNet, BiSeNet, AFN, and FastFCN. The experimental results show that MFAGNet is superior to the comparison models. With no significant increase in computing overhead, MFAGNet obtains MIoU of 69.05%, 73.40%, and 79.42% on the standard data sets GID, Vaihingen, and Potsdam, respectively.

CLASSIFICATION OF DISTRACTED DRIVER USING SUPPORT VECTOR MACHINE BASED ON PRINCIPAL COMPONENT ANALYSIS FEATURE REDUCTION AND CONVOLUTIONAL NEURAL NETWORK

The use of ground transportation in Indonesia, especially in major cities like Surabaya, has experienced rapid growth. However, this increased usage has also led to a rise in traffic accidents. One of the main contributing factors is driver distraction. Therefore, this study aims to develop a method for detecting distracted drivers using image classification technology with Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) models. In this research, data was obtained from the "State Farm Distracted Driver Detection" dataset, which contains images of drivers who are either distracted or not focused while driving. The initial process involves data preprocessing, such as resizing images to 50 x 50 pixels and dividing the dataset into training and testing data. Next, feature extraction is performed using a CNN model with three convolutional layers, three Maxpooling layers, and one flattened layer. After feature extraction, the Principal Component Analysis (PCA) method is used to reduce the dimensionality of the data. Furthermore, an SVM model was trained using data reduced by PCA with a 60:40 data split. This research conducted a comparison between the use of PCA and not using PCA. Based on the test results, the use of PCA not only improved the classification accuracy to 96.28% compared to 92.46% without PCA but also accelerated the training time to 10.64 seconds from 19.67 seconds without PCA.

XCNN-SC: Explainable CNN for SARS-CoV-2 variants classification and mutation detection

The COVID-19 pandemic spread rapidly all over the world in 2019, causing the deaths of millions of people. One of the main challenges in controlling the pandemic was the high rate of virus mutation, which evaded the immune system and reduced the vaccine's effectiveness. Due to the differences in symptoms, transmission and mortality rate, and effective prevention strategies of each variant, identifying and classifying different variants is a great necessity. In the present study, 82802 whole genomes of Alpha, Beta, Delta, Eta, Epsilon, Lota, and Omicron variants of SARS-CoV-2 were obtained from the NCBI database. The label encoding method was applied to convert the genomic sequences to numeric sequences. Then, a single-layer 1D-CNN model was used to classify seven variants of SARS-CoV-2 and achieved 99.78% accuracy, 97.98% precision, 97.66% sensitivity, 99.95% specificity, and 98.68% F1-score. The max pool layer feature maps of this network were investigated to discover the sequence discriminative regions in SARS-CoV-2 variants and their effect on biological functional differences. The feature map examination led to the detection of three mutations; one of them was a missense mutation, which has been reported previously. The rest of the detected mutations were silent mutations. In conclusion, the achieved results indicated that CNN models can extract effective features to discriminate several SARS-CoV-2 variants. Also, investigation of CNN feature maps led to an explainable CNN revealing the learned knowledge of the network from the training database. This knowledge helped us to detect mutations and their functional effects in different variants.

A Novel Paradigm for Enhancing Linear Model Predictions across Multivariate Functions through Integration of CNNs

Linear models have been a cornerstone in statistical data analysis, yet their inherent linearity often constrains their ability to extract nuanced patterns from complex datasets. This limitation poses a challenge in real-world applications where non-linearity prevails. In this research, we introduce an innovative methodology aimed at overcoming this deficiency. Leveraging the powerful non-linear modelling capabilities of Convolutional Neural Networks(CNNs), we augment the predictive capabilities of linear models. Through a systematic approach, we extend our findings to encompass specific classes of crucial multivariate functions. Our investigation primarily focuses on neural network architectures that integrate convolution (conv), Rectified Linear Unit (ReLU) activation functions, and max pooling layers. By harnessing the synergies of these components, we unveil a novel paradigm for enhancing linear model predictions, unlocking a wealth of potential applications across diverse domains. This research lays the foundation for a more robust and accurate predictive modelling framework that transcends the boundaries of conventional linear approaches.

Airborne hyperspectral imaging for early diagnosis of kimchi cabbage downy mildew using 3D-ResNet and leaf segmentation

Kimchi cabbage (Brassica rapa pekinensis), one of the main agricultural products in Korea, is susceptible to downy mildew disease infections. Infected plants develop yellow spots (chlorosis) on the upper (adaxial) side of the infected leaf, undermining cabbage production and quality. An early detection method to recognize and treat the disease is crucial to prevent downy mildew and lessen its physical effects on plants. Hyperspectral imaging can capture data from a broad spectrum, which can be utilized to detect disease occurrence before any visible symptoms appear. Combining a hyperspectral camera with an unmanned aerial vehicle (UAV) can provide a non-destructive, field-scale disease detection system. In this study, three-dimensional (3D) convolutional neural network (CNN) models were used to simultaneously account for the spectral and spatial features of the disease to enable automatic disease detection. Using a 3D-residual network (ResNet) CNN with four residual blocks, each followed by a rectified linear unit activation function and a max-pooling layer, helped achieve an overall accuracy of 0.876 and a diseased class accuracy of 0.873. Disease severity was estimated by grouping nearby diseased leaves using the density-based spatial clustering of applications with noise clustering algorithm to achieve a 27.07 % relative error or a 1.08 level difference from the actual.

An MRI-Based Convolutional Neural Network to Predict Biochemical Recurrence Following Radiotherapy for Intermediate and High-Risk Localized Prostate Cancer.

The risk of biochemical recurrence (BCR) following radiotherapy (RT) for localized prostate cancer (LPCa) varies considerably within risk stratification groups defined by classic clinical and pathologic variables; there is an unmet need for low-cost tools that more robustly predict BCR and allow for individualized therapy. Published imaging-based algorithms for BCR prediction after RT are limited to hand-crafted radiomics and/or small cohorts. We aimed to develop a deep learning model to predict BCR at 5 years after RT for intermediate and high risk LPCa using pre-treatment T2-weighted (T2W) MRI. Patients with intermediate and high risk LPCa treated with radical RT at our institution between 2010 and 2015 were included. We excluded those who did not have a pre-treatment T2W-MRI and those with less than 5 years of follow-up. The Phoenix definition for BCR was used. The dataset (DS1) was split into training (70%), validation (20%), and test (10%) sets using a stratified technique. A U-Net model for prostate segmentation was trained and tested on a separate annotated prostate T2W-MRI dataset (DS2) of 225 patients from our institution. The U-Net model was then used to segment the whole prostate gland on the MRI images of DS1, and the segmented images were fed into four 2D convolutional neural networks (CNNs) using different network architectures and regularization techniques (VGG blocks with batch normalization, dropout, and max pooling layers) to predict BCR at 5 years. The CNNs were evaluated using the area under the receiver operating characteristic curve (AUC) on the test set. For benchmarking, three machine learning classifiers (Random Forest, Logistic Regression, and Support Vector Machines) were developed using the 5 most important features selected by Mean Decrease in Impurity from a set of 18 clinical variables. A total of 189 patients were included in DS1. Androgen deprivation therapy (ADT) was received by 83.6% of patients. BCR was identified in 26% of the cases. The Dice score for the U-Net segmentation model was 78% on the test set of DS2. The AUC achieved by the different CNNs for predicting BCR ranged between 0.53 and 0.75. The best performing CNN consisted of 3 convolutional layers, the first two followed by max-pooling layers, a flattening layer, a dense layer, and an output layer with softmax activation function. The best clinical model was a Random Forest algorithm with an AUC of 0.70. The selected clinical variables by decreasing feature importance were: age, time to nadir PSA, pre-treatment PSA, percentage of positive biopsy cores at diagnosis, and nadir PSA. We developed a deep learning model based on pre-treatment T2W-MRI to predict BCR at 5 years following radical RT for intermediate and high-risk LPCa. This CNN outperformed a model based on clinical variables and warrants further validation in external cohorts.

Classification of Quality Characteristics of Surimi Gels from Different Species Using Images and Convolutional Neural Network

In the aspect of food quality measurement, the application of image analysis has emerged as a powerful and versatile tool, enabling a highly accurate and efficient automated recognition and the quality classification of visual data. This study examines the feasibility of employing an AI algorithm on labeled images as a non-destructive method to classify surimi gels. Gels were made with different moisture (76–82%) and corn starch (5–16%) levels from Alaska pollock and Threadfin breams. In surimi gelation, interactions among surimi, starch, and moisture caused color and quality shifts. Color changes are indicative of structural and quality variations in surimi. Traditional color measuring techniques using colorimeter showed insignificant differences (p < 0.05) in color values and whiteness among treatments. This complexity hindered effective grading, especially in intricate formulations. Despite insignificant color differences, they signify structural changes. The Convolutional Neural Network (CNN) predicts the visual impact of moisture and starch on gel attributes prepared with different surimi species. Automated machine learning assesses AI algorithms; and CNN’s 70:30 training/validation ratio involves 400–700 images per category. CNN’s architecture, including input, convolutional, normalization, Rectified Linear Unit (ReLU) activation, and max-pooling layers, detects subtle structural changes in treated images. Model test accuracies exceed 95%, validating CNN’s precision in species and moisture classification. It excels in starch concentrations, yielding > 90% accuracy. Average precision (>0.9395), recall (>0.8738), and F1-score (>0.8731) highlight CNN’s high performance. This study demonstrates CNN’s value in non-destructively classifying surimi gels with varying moisture and starch contents across species, and it provides a solid foundation for advancing our understanding of surimi production processes and their optimization in the pursuit of high-quality surimi products.

MPD-APP: a mobile-enabled plant diseases diagnosis application using convolutional neural network toward the attainment of a food secure world.

The devastating effect of plant disease infestation on crop production poses a significant threat to the attainment of the United Nations' Sustainable Development Goal 2 (SDG2) of food security, especially in Sub-Saharan Africa. This has been further exacerbated by the lack of effective and accessible plant disease detection technologies. Farmers' inability to quickly and accurately diagnose plant diseases leads to crop destruction and reduced productivity. The diverse range of existing plant diseases further complicates detection for farmers without the right technologies, hindering efforts to combat food insecurity in the region. This study presents a web-based plant diagnosis application, referred to as mobile-enabled Plant Diagnosis-Application (mPD-App). First, a publicly available image dataset, containing a diverse range of plant diseases, was acquired from Kaggle for the purpose of training the detection system. The image dataset was, then, made to undergo the preprocessing stage which included processes such as image-to-array conversion, image reshaping, and data augmentation. The training phase leverages the vast computational ability of the convolutional neural network (CNN) to effectively classify image datasets. The CNN model architecture featured six convolutional layers (including the fully connected layer) with phases, such as normalization layer, rectified linear unit (RELU), max pooling layer, and dropout layer. The training process was carefully managed to prevent underfitting and overfitting of the model, ensuring accurate predictions. The mPD-App demonstrated excellent performance in diagnosing plant diseases, achieving an overall accuracy of 93.91%. The model was able to classify 14 different types of plant diseases with high precision and recall values. The ROC curve showed a promising area under the curve (AUC) value of 0.946, indicating the model's reliability in detecting diseases. The web-based mPD-App offers a valuable tool for farmers and agricultural stakeholders in Sub-Saharan Africa, to detect and diagnose plant diseases effectively and efficiently. To further improve the application's performance, ongoing efforts should focus on expanding the dataset and refining the model's architecture. Agricultural authorities and policymakers should consider promoting and integrating such technologies into existing agricultural extension services to maximize their impact and benefit the farming community.

New technologies for UAV navigation with real-time pattern recognition

The study is dedicated to the investigation and analysis of real-time navigation technologies for UAVs utilizing image scanning and object recognition. This research employs the DJI Inspire 2 drone alongside MapBotix software, integrating adapted algorithms such as Faster R-CNN and MaskRCNN. During the flight of the DJI Inspire 2 drone over the Banska Bystrica region, information concerning object recognition and change detection within the studied area was collected. A 40-second scanning process documented two alterations, one of which occurred at the 10th second. Subsequent stages of image recognition were executed utilizing scaling techniques, thus ensuring the accuracy of data extracted from captured images. This research, in particular, pioneers the exploration of real-time UAV navigation technologies integrated with image recognition. The application of a customized MaskRCNN algorithm, comprising 9 convolutional layers, 4 max-pooling layers, and 1 detection layer, for analyzing scanned object images signifies a novel approach in this domain.

Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks

Aim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods. Material and Methods: In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse. Results: The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset. Conclusion: Continuous research and optimization of the model can further improve its performance and benefit the field of cumulus-oocyte complexes classification and oocyte quality assessment.

An exact mapping from ReLU networks to spiking neural networks

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance.

Music classification with convolutional and artificial neural network

Music genre classification focus on efficiently finding expected music with a similar genre through numerous melodies, which could better satisfy the tastes and expectations of the users when listening to music. This paper proposes a new method to classify different kinds of music with Artificial Neural networks (ANN) and Convolutional Neural Networks (CNNs). First, Mel Frequency Cepstral Coefficients (MFCC) are used to preprocess the Mel-frequency cepstrum (MFC). Then, we upgrade Anupam’s CNN model. Since the extracted features only by MFC are not suitable for CNN to learn as a small dataset like this. Multiple features are then exacted for each audio file. The two most correlated features on the datasets are adopted as the input of an ANN. To verify the proposed method’s effectiveness, we compare our method with other state-of-the-art methods on the GTZAN dataset. The experimental results show that we can get higher accuracy compared to Anupam. If using only one MFCC feature, Conv-Conv-Pool, a sub-structure that we add two convolutional layers before each max pooling layer, performs better than Conv-Pool, and Conv-Pool performs better than ANN. However, by concatenating another correlated feature, spectral centroid means, which is a measure used in digital signal processing to characterize a spectrum, a simple ANN can have much higher accuracy than the one utilizing only a single MFCC feature with an accuracy of about 94.1%.

Time frequency domain deep CNN for automatic background classification in speech signals

Many application areas, such as background identification, predictive maintenance in industrial applications, smart home applications, assisting deaf people with their daily activities and indexing and retrieval of content-based multimedia, etc., use automatic background classification using speech signals. It is challenging to predict the background environment accurately from speech signal information. Thus, a novel synchrosqueezed wavelet transform (SWT)-based deep learning (DL) approach is proposed in this paper for automatically classifying background information embedded in speech signals. Here, SWT is incorporated to obtain the time-frequency plot from the speech signals. These time-frequency signals are then fed to a deep convolutional neural network (DCNN) to classify background information embedded in speech signals. The proposed DCNN model consists of three convolution layers, one batch-normalization layer, three max-pooling layers, one dropout layer, and one fully connected layer. The proposed method is tested using various background signals embedded in speech signals, such as airport, airplane, drone, street, babble, car, helicopter, exhibition, station, restaurant, and train sounds. According to the results, the proposed SWT-based DCNN approach has an overall classification accuracy of 97.96 (± 0.53)% to classify background information embedded in speech signals. Finally, the performance of the proposed approach is compared to the existing methods.

Predictability of Off-Targets in CRISPR-Cas9 Gene Editing Systems using Convolutional Neural Networks

CRISPR-Cas systems have catalyzed a quantum leap in genomics, providing a remarkable degree of precision in genomic sequence alterations. Despite these advancements, one notable conundrum persists - the prediction and mitigation of off-target effects. These unintended effects occur when the CRISPR apparatus interacts with homologous, albeit non-identical sequences, which could potentially induce unanticipated mutations. This investigation employs a comparative approach, juxtaposing machine-learning models that harness sophisticated Convolutional Neural Networks with different architectures. The standard CNN model utilizes a stratified architecture that meticulously filters pertinent genetic patterns, and identifies salient genetic features using a convolutional layer along with a batch normalization layer and a max pooling layer. This architecture prevents overfitting by applying a dropout layer, and 2 dense layers to categorize the two outputs. In contrast, the AttnToMismatch_CNN model marries the attention mechanism with the convolutional paradigm to encode sgRNA and DNA sequences into vector representations by using embedding and transformer layers. Which is then passed into a convolution layer and then to a dense layer to result in two outputs. Performance appraisal of the models, through the Area Under the Curve (AUC) of the Receiver Operating Characteristic Curve (AUC-ROC) score, indicated the standard CNN model's superior predictive accuracy. This research accentuates the untapped potential of Convolutional Neural Networks in augmenting the predictability of off-target effects in CRISPR-Cas systems, thereby fostering safer and more efficacious applications of this transformative gene editing tool.

Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images

Diabetic retinopathy (DR), a long-term complication of diabetes, is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms. Standard diagnostic procedures for DR now include OCT and digital fundus imaging. If digital fundus images alone could provide a reliable diagnosis, then eliminating the costly optical coherence tomography would be beneficial for all parties involved. Optometrists and their patients will find this useful. Using deep convolutional neural networks, we provide a novel approach to this problem. Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones. In order to collect more subtle information for categorisation, two such DCNNs, each with a different number of layers, are trained. To establish these limits, we use deep convolutional neural networks (DCNNs) and features extracted from picture metadata to train a support vector machine classifier. In our experiments, we used information from Kaggle's open DR detection database. We fed our model 34,124 training images, 1,000 validation examples, and 53,572 test images to train and test it. Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4. Experimental results show a higher identification rate (86.17%) than those found in the existing literature, indicating the suggested strategy may be effective. We have jointly developed an algorithm for machine learning and accompanying software, and we've named it Deep Retina. Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology. This technology might be used for self-diagnosis, at-home care, and telemedicine.

Classification of Chest X-ray Images using CNN for Medical Decision Support System

X-rays are a crucial tool used by healthcare professionals to diagnose a range of medical conditions. However, it is important to keep in mind that a timely and accurate diagnosis is crucial for effective patient management and treatment. While chest X-rays can provide highly precise anatomical data, manual interpretation of the images can be time-consuming and prone to errors, which can lead to delays or incorrect diagnoses. To address these issues, healthcare systems have taken steps to improve diagnostic imaging services following the impact of the COVID-19 pandemic. While deep learning-based automated systems for classifying chest X-rays have shown promise, there are still several challenges that need to be addressed before they can be widely used in clinical settings, including the lack of comprehensive and high-quality datasets. To overcome these limitations, a real-time DICOM dataset, has been converted to JPEG format to increase processing speed and improve data control. Three pre-trained models and a convolutional neural network (CNN) model with low complexity and three convolutional layers for feature extraction, along with max pooling layers and ReLU and Softmax activation functions have been implemented. With an validation accuracy of 95.05% on their CNN model using the SGD optimizer, the result has been validated using a separate, real-time unlabeled DICOM dataset of 1000 X-ray images.