Deep Learning Convolutional Neural Network Research Articles

Enhancing Arabic Sign Language Recognition using Deep Learning

The present time, Sign language is very important for people who suffer from hearing loss or who cannot speak. Normal humans tend to disregard the significance of signal language, which is a mere supply of communique to mute and deaf societies. So, this study proposes a developed model for sign Language Recognition for Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then set the algorithm by developing programming on Open-CV, using Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, and each one of them included 1500 images incorporating hand gestures in at-variance environments. The data set was divided into a training section with a percentage of 70 %, a section for testing with a percentage of 20 %, and a section for validation with a percentage of 10 %. The results show that the suggested model achieved an accuracy rate of 94.8%, and it has proven its effectiveness and success, especially after being tried and tested by several users and obtaining their comments and feedback.

An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

In this paper, an efficient methodology for detecting and classifying tomato diseases using a Convolutional Neural Network (CNN) deep learning network is presented as an efficient aided tool to classify different tomato diseases based on their leaf appearence, where plant diseases and insects are considered as a main challenges for farmers to overcome. The proposed methodology structure is based on 20 layers using convolution, Maxpooling, Batch normalization and ReLU process as main operations in the adopted architecture. The obtained results using Plant Village database show that our proposed methodology outclasses the best recent methods of tomato diseases detection and classification with scores of 97.3% 96.8% 97.0% and 97.5% for precision, Recall, F1-Score and accuracy coefficient criteria respectively. Our methodology of tomato diseases detection and classification is found as effective, accurate and aided diagnostic tool that aims to aid farmers to make a precise plant treatments and enhancing productivity while promoting environmental sustainability.

Initial Fire Detection Method for Intelligent CCTV Using Deep Learning Technology

Pre-prediction and early detection are important for the prevention of fires. Hence, the integration of intelligent CCTV image analysis technology, deep learning, and artificial intelligence is actively being studied. Intelligent CCTV can analyze visible light images of fires, but the accuracies of the detection targets are low. To solve this problem, a dual sensing experimental device was constructed by combining a visible light sensor and a thermal image sensor, and an algorithm was implemented to predict fire events. An algorithm was constructed to detect three classes (Fire, Smoke, and Person) through RGB sensors and thermal imaging sensors. Additionally, a CNN deep learning method and the YOLOv5 method were used to build a classification neural network. The dual sensing device was able to act as a trigger to predict and detect a fire based on the size of the flame and changes in temperature.

Comparison of deep learning models for weather forecasting in different climatic zones

Weather forecasting has become an integral part of our day-to-day life. Weather holds significant importance in our everyday lives, impacting areas such as how we travel, produce food, and maintain public well-being. Mostly, weather prediction is done with machines learning models, but the use of deep learning techniques in this field in growing. Still, the existing studies are not sufficient to get a clear concept of weather prediction in different climatic zones. Therefore, in this study, selected four deep learning models, RNN, CNN and LSTM, to predict temperature in four climatic zones. We selected four cities, Dhaka, Moscow, Dubai and Brasilia from four different climatic zones. It is seen that the overall accuracy (OA) of LSTM ranged between 85% to 95%, followed by CNN 78% to 91%, and RNN 64% to 94%. Though the OA values of these three models in four climatic zones differs significantly, high AUC values were seen in all scenario. The highest AUC value (0.999) was seen in continental climatic zone for LSTM model and lowest (0.963) in mil climatic zone for RNN.

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs.

Stellar populations with optical spectra: deep learning versus popular spectrum fitting codes

ABSTRACT We compare the performance of several popular spectrum fitting codes (firefly, starlight, pypipe3d, and ppxf), and a deep-learning convolutional neural network (starnet), in recovering known stellar population properties (mean stellar age, stellar metallicity, stellar mass-to-light ratio M*/Lr and the internal E(B−V)) of simulated galaxy spectra in optical wavelengths. Our mock spectra are constructed from star-formation histories from the IllustrisTNG100-1 simulation. These spectra mimic the Sloan Digital Sky Survey (SDSS) through a novel method of including the noise, sky residuals, and emission lines taken directly from SDSS. We find that starnet vastly outperforms all conventional codes in both speed and recovery of stellar population properties (error scatter <0.08 dex, average biases <0.02 dex for all tested quantities), but it requires an appropriate training set. Of the non-machine-learning codes, ppxf was a factor of 3–4 times faster than the other codes, and was the best in recovering stellar population properties (error scatter of <0.11 dex, average biases <0.08 dex). However, the errors and biases are strongly dependent on both true and predicted values of stellar age and metallicity, and signal-to-noise ratio. The biases of all codes can approach 0.15 dex in stellar ages, metallicities and log M*/Lr, but remain ≲0.05 for E(B−V). Using unrealistic Gaussian noise in the construction of mock spectra will underestimate the errors in the metallicities by a factor of 2 or more, and mocks without emission lines will underestimate the errors in stellar age and M*/Lr by a factor of 2.

Robust Deepfake Detection System with Deep Learning Techniques

This comprehensive study investigates the pervasive issue of deep fakes within the context of deep learning applications, focusing on their detection and production. Utilizing a diverse array of deep learning algorithms, including InceptionResnetV2, VGG19, CNN, Xception, InceptionV3, EfficientNetB1, DenseNet121, Hybrid Model, LSTM, ResNext-LSTM, and MRI-GAN, the research systematically evaluates their effectiveness in detecting deep fakes. Results reveal varying levels of accuracy, with Xception emerging as the most precise algorithm, achieving an accuracy of 99.32%. Notably, InceptionResnetV2 and DenseNet121 also demonstrate robust performance, with accuracies surpassing 99%. However, certain models like VGG19 and LSTM exhibit lower accuracy rates, underscoring the need for further refinement. These findings underscore the urgent necessity for robust detection mechanisms amidst the proliferation of malicious deep fakes, safeguarding against potential societal ramifications such as misinformation and privacy breaches.

An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration

To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study

BackgroundCombining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.MethodsA total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.ResultsThe comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.ConclusionsThe novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.Trial registration: Not applicable.

IOT based Waste Segregation Using Deep Learning

In the rapidly urbanizing world, effective waste management is crucial to maintain environmental sustainability. This project presents an innovative solution utilizing Internet of Things (IoT) technology and deep learning Convolutional Neural Networks (CNN) algorithm for automated waste segregation. We propose a smart waste segregation system equipped with sensors to detect the level of waste in bins. The system utilizes a CNN model trained using Deep Learning to classify waste items into different categories, ensuring proper segregation. When a bin reaches its full capacity, the IoT sensors trigger an alert mechanism, sending real-time notifications to the concerned authorities or waste management personnel. This proactive approach not only optimizes waste collection but also promotes recycling by ensuring the segregated waste reaches the appropriate recycling facilities. The integration of IoT devices and deep learning algorithms enhances the efficiency of waste management processes, reducing environmental pollution and promoting a sustainable future. Key Words: IoT, waste management, deep learning, CNN algorithm, waste segregation, environmental sustainability, real-time monitoring, sensor technology, recycling, smart cities, automation.

A Bidirectional Arabic Sign Language Framework Using Deep Learning and Fuzzy Matching Score

Sign language is widely used to facilitate the communication process between deaf people and their surrounding environment. Sign language, like most other languages, is considered a complex language which cannot be mastered easily. Thus, technology can be used as an assistive tool to solve the difficulties and challenges that deaf people face during interactions with society. In this study, an automatic bidirectional translation framework for Arabic Sign Language (ArSL) is designed to assist both deaf and ordinary people to communicate and express themselves easily. Two main modules were intended to translate Arabic sign images into text by utilizing different transfer learning models and to translate the input text into Arabic sign images. A prototype was implemented based on the proposed framework by using several pre-trained convolutional neural network (CNN)-based deep learning models, including the DenseNet121, ResNet152, MobileNetV2, Xception, InceptionV3, NASNetLarge, VGG19, and VGG16 models. A fuzzy string matching score method, as a novel concept, was employed to translate the input text from ordinary people into appropriate sign language images. The dataset was constructed with specific criteria to obtain 7030 images for 14 classes captured from both deaf and ordinary people locally. The prototype was developed to conduct the experiments on the collected ArSL dataset using the utilized CNN deep learning models. The experimental results were evaluated using standard measurement metrics such as accuracy, precision, recall, and F1-score. The performance and efficiency of the ArSL prototype were assessed using a test set of an 80:20 splitting procedure, obtaining accuracy results from the highest to the lowest rates with average classification time in seconds for each utilized model, including (VGG16, 98.65%, 72.5), (MobileNetV2, 98.51%, 100.19), (VGG19, 98.22%, 77.16), (DenseNet121, 98.15%, 80.44), (Xception, 96.44%, 72.54), (NASNetLarge, 96.23%, 84.96), (InceptionV3, 94.31%, 76.98), and (ResNet152, 47.23%, 98.51). The fuzzy matching score is mathematically validated by computing the distance between the input and associative dictionary words. The study results showed the prototype’s ability to successfully translate Arabic sign images into Arabic text and vice versa, with the highest accuracy. This study proves the ability to develop a robust and efficient real-time bidirectional ArSL translation system using deep learning models and the fuzzy string matching score method.

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

ABSTRACT Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades

A precise and cost-effective prediction tool for fluid-structure interaction (FSI) analysis is crucial for optimizing the structural design of tidal turbine blades. However, the high computational costs associated with fluid dynamic analysis pose a significant challenge, as the current lack of efficient FSI prediction methods hinders the advancement of cutting-edge tidal turbine designs. To address this issue, this paper proposes a novel consolidated framework that integrates deep learning convolutional neural networks (CNN) with blade element momentum (BEM) theory and finite element method (FEM) to perform deformation analysis of turbine blade structures. The proposed CNN-BEM-FEM integrated framework efficiently identifies the geometric features and predicts the hydrodynamic parameters of turbine blades and thus, achieving accurate assessments of the structural behavior of tidal turbines. The study applies two-step verification procedures to validate the prediction accuracy of the CNN-BEM-FEM framework and the result demonstrates excellent agreement with experimental tests for hydrodynamic performance and blade deformation. When compared with the static one-way FSI calculated by Ansys Workbench software, the computational efficiency of CNN-BEM-FEM framework increases by more than 18 times, with discrepancies in blade deformation and equivalent stress calculations generally less than 5 %. By applying the proposed method to predict the FSI performance of tidal turbine blades with various shear web structures, the practical applicability for composite turbine blade design is successfully demonstrated. The results underscore the potential of the CNN-BEM-FEM framework as an efficient and accurate prediction tool for optimizing the structural design of tidal turbine blades.

Udder thermogram-based deep learning approach for mastitis detection in Murrah buffaloes

Mastitis, a production disease with multiple etiologies, inflicts significant economic losses among dairy farmers around the globe. In this study, an attempt has been made to detect mastitis through a Convolutional Neural Networks (CNN)-based deep learning model using 7615 udder thermograms of 40 Murrah buffaloes. The thermograms were grouped separately as healthy, sub-clinical (SCM), and clinical mastitis (CM) affected udder quarters based on California Mastitis Test (CMT) scores, Somatic Cell Count (SCC) values and thermal image analysis. Results of thermogram analysis revealed a significant increase (p < 0.01) in the mean values of udder skin surface temperature (USST) among SCM and CM-affected quarters compared to healthy quarters to the tune of 1.32 and 2.61 °C, respectively. The USST showed a strong positive correlation with the CMT score (r = 0.87, p < 0.01) and log10SCC value (r = 0.88, p < 0.01). The sequential (Normal vs. Clinical and Normal vs. Sub-clinical) models had training accuracy and validation accuracy of 0.999 and 0.988, 0.991 and 0.978, respectively. The confusion matrix for Normal vs. Clinical and Normal vs. Sub-clinical models reflected a loss of 0.009 and 0.029, precision of 0.947 and 0.980, and recall of 0.996 and 0.904, respectively. Consequently, the sequential (Normal vs. Clinical and Normal vs. Sub-clinical) models achieved a testing accuracy of 0.970 and 0.943, respectively. Thus, the improved deep-learning CNN models efficiently predicted SCM and CM cases in Murrah buffaloes.

Fruit Quality Analysis and Disease Detection using Deep Learning Techniques

Defective fruits are the main reason for worldwide financial catastrophes in agricultural production. It affects both the dependability and quality of the fruits. Post-harvest, quality checking requires a significant amount of time and labor-intensive skill. Automatically identifying fruit quality enables saving time and labor during harvest. Various algorithms have been created using machine learning and image processing methods to detect and categorize fruit quality. A system using Convolutional Neural Networks (CNN) and transfer learning techniques has been developed to enhance the fruit classification process. Two methods are suggested for predicting fruit freshness. One tailored CNN architecture is proposed by modifying the network's parameters to suit the dataset. The second technique utilizes the pre-trained VGG model with transfer learning to assess the freshness of the fruit. The proposed models are capable of differentiating between fresh and spoiled fruit by analyzing the input photos. This research used 70 diverse types of fruit, such as apples, bananas, oranges, and others. The first CNN obtains 98.55% accuracy for heterogeneous fruit dataset while second VGG16 achieves 99.05% accuracy on similar dataset. The plant village global dataset is utilized with various fruit categories. As a result, The VGG16 provides higher accuracy than conventional CNN and other deep learning algorithms.

Predictive Modeling of Infection from Chest X-ray Images with ResNet34 and Deep learning CNN approach

The timely and precise detection of infected persons is still a major issue in the ongoing fight against the COVID-19 epidemic. With its ability to provide information on the severity and development of the illness, chest X-ray imaging has become a useful diagnostic and surveillance technique for COVID-19 pneumonia. This paper suggests a unique method for predictive modeling of COVID-19 infection from chest X-ray pictures by using the capabilities of deep learning, namely ResetNet architecture. In image classification tasks, ResNet34, a convolutional neural network (CNN) variation, has shown impressive performance, particularly in situations with little training data. Here, we report a comprehensive training and validation protocol for the ResetNet model using a large dataset of chest X-ray images that includes both positive and negative COVID-19 instances. To improve feature extraction, the dataset is preprocessed. All data size: 5856,Train size: 4684, Val size: 585, Test size: 587. To reduce overfitting and boost generalization, data augmentation methods are used. Our suggested method uses chest X-ray pictures to reliably distinguish COVID-19 patients from non-COVID-19 cases by using deep learning's discriminative capabilities. The result of model with TRAINING: Epoch: 19, Loss: 0.08099747663349896, Accuracy: 97.11and TEST: Epoch: 19, Loss: 0.08553645759820938, Accuracy: 96.76.

LAMA-WAVELET: IMAGE IMPAINTING WITH HIGH QUALITY OF FINE DETAILS AND OBJECT EDGES

Context. The problem of the image impainting in computer graphic and computer vision systems is considered. The subject of the research is deep learning convolutional neural networks for image inpainting.&#x0D; Objective. The objective of the research is to improve the image inpainting performance in computer vision and computer graphics systems by applying wavelet transform in the LaMa-Fourier network architecture.&#x0D; Method. The basic LaMa-Fourier network decomposes the image into global and local texture. Then it is proposed to improve the network block, processing the global context of the image, namely, the spectral transform block. To improve the block of spectral transform, instead of Fourier Unit Structure the Simple Wavelet Convolution Block elaborated by the authors is used. In this block, 3D wavelet transform of the image on two levels was initially performed using the Daubechies wavelet db4. The obtained coefficients of 3D wavelet transform are splitted so that each subband represents a separate feature of the image. Convolutional layer, batch normalization and ReLU activation function are sequentially applied to the results of splitting of coefficients on each level of wavelet transform. The obtained subbands of wavelet coefficients are concatenated and the inverse wavelet transform is applied to them, the result of which is the output of the block. Note that the wavelet coefficients at different levels were processed separately. This reduces the computational complexity of calculating the network outputs while preserving the influence of the context of each level on image inpainting. The obtained neural network is named LaMa-Wavelet. The FID, PSNR, SSIM indexes and visual analysis were used to estimate the quality of images inpainted with LaMa-Wavelet network.&#x0D; Results. The proposed LaMa-Wavelet network has been implemented in software and researched for solving the problem of image inpainting. The PSNR of images inpainted using the LaMa-Wavelet exceeds the results obtained using the LaMa-Fourier network for narrow and medium masks in average by 4.5%, for large masks in average by 6%. The LaMa-Wavelet applying can enhance SSIM by 2–4% depending on a mask size. But it takes 3 times longer to inpaint one image with LaMa-Wavelet than with LaMa-Fourier network. Analysis of specific images demonstrates that both networks show similar results of inpainting of a homogeneous background. On complex backgrounds with repeating elements the LaMa-Wavelet is often more effective in restoring textures.&#x0D; Conclusions. The obtained LaMa-Wavelet network allows to improve the image inpainting with large masks due to applying wavelet transform in the LaMa network architecture. Namely, the quality of reconstruction of image edges and fine details is increased.

Detecting multilingual hate speech targeting immigrants and women on Twitter

Twitter has experienced a tremendous surge in popularity over recent years, establishing itself as a prominent social media platform with a large user base. However, with this increased usage, there has been a concerning rise in the number of individuals resorting to derogatory language and expressing their opinions in a demeaning manner toward others. This surge in hate speech has drawn significant attention to the field of sentiment analysis, which aims to develop algorithms capable of detecting and analyzing emotions expressed in social networks using intuitive approaches. This paper focuses on addressing the complex task of detecting hate speech and aggressive behavior while performing target classification. We explored various deep-learning approaches, including LSTM, BiLSTM, CNN, and GRU. Each offers unique capabilities for capturing different aspects of the input data. We proposed an ensemble approach that combines the top three performing models. This ensemble approach benefits from the diverse strengths of each individual model showing F1 score of 0.85 for English-HS, 0.94 for English-TR, 0.92 for English-AB, 0.84 for Spanish-HS, 0.86 for Spanish-TR, 0.97 for Spanish-AB, 0.74 for multilingual-HS, 0.94 for multilingual-TR, and 0.88 for multilingual-AB.

EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often perform in-silico prediction to select low hemolytic peptides before proceeding with in-vitro testing. The in-silico tools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work. Proposed framework utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely Acc, Sn, Pr, Fs, Sp, Ba, and Mcc obtained by proposed framework for test data is ≈ 87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server at https://endl-hemolyt.anvil.app/.

Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds

BackgroundImaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images.MethodsA transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation.ResultsThe DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer.ConclusionsThis research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.