Deep Learning-based Convolutional Neural Network Research Articles

Hexagonal diffraction gratings generated by convolutional neural network-based deep learning for suppressing high-order diffractions

Hexagonal diffraction gratings generated by convolutional neural network-based deep learning for suppressing high-order diffractions

Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography.

Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies.

Topographic Clinical Insights From Deep Learning-Based Geographic Atrophy Progression Prediction.

To explore the contributions of fundus autofluorescence (FAF) topographic imaging features to the performance of convolutional neural network-based deep learning (DL) algorithms in predicting geographic atrophy (GA) growth rate. Retrospective study with data from study eyes from three clinical trials (NCT02247479, NCT02247531, NCT02479386) in GA. The algorithm was initially trained with full FAF images, and its performance was considered benchmark. Ablation experiments investigated the contribution of imaging features to the performance of the algorithms. Three FAF image regions were defined relative to GA: Lesion, Rim, and Background. For No Lesion, No Rim, and No Background datasets, a single region of interest was removed at a time. For Lesion, Rim, and Background Shuffled datasets, individual region pixels were randomly shuffled. For Lesion, Rim, and Background Mask datasets, masks of the regions were used. A Convex Hull dataset was generated to evaluate the importance of lesion size. Squared Pearson correlation (r2) was used to compare the predictive performance of ablated datasets relative to the benchmark. The Rim region influenced r2 more than the other two regions in all experiments, indicating the most relevant contribution of this region to the performance of the algorithms. In addition, similar performance was observed for all regions when pixels were shuffled or only a mask was used, indicating intensity information was not independently informative without textural context. These ablation experiments enabled topographic clinical insights on FAF images from a DL-based GA progression prediction algorithm. Results from this study may lead to new insights on GA progression prediction.

Novel Antenna for Partial Discharge Detection and Classification: A Convolutional Neural Network-Based Deep Learning Approach

Novel Antenna for Partial Discharge Detection and Classification: A Convolutional Neural Network-Based Deep Learning Approach

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Abstract For safety, reliability, and uninterrupted output of gas turbines, aviation engines, power-generating equipment, pumps, gears, compressors etc., rotor mass imbalance must be detected and diagnosed to avoid catastrophic failure. Industry 4.0 relies on predictive digital maintenance and deep learning-based convolutional neural network (CNN), which predicts defects but fails if the operating conditions change. Research studies in various fields indicate that the domain shift issue occurs due to source and target samples being from different domains, which reduces prediction capability. Moreover, research studies are scarce in examining prediction capability under varying operating speeds for rotor mass imbalance. Hence, this research proposes the adversarial discriminative domain adaptation (ADDA) technique which predicts machine failures under various operational conditions. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA's architecture to take advantage of CNN's feature extraction capability. Further, this research effectively tackles CNN's inherent issues of overfitting and hyperparameters value selection. Furthermore, The real-world scenario has more healthy samples than fault condition samples, causing a multiclass imbalance in sample data, which affects the classification decision boundary and causes biased prediction. Hence, the proposed methodology first addresses the class imbalance through synthetic minority oversampling (SMOTE), then genetic algorithm optimizes 1D-CNN's hyperparameters, and the effective dropout layer positioning solves the overfitting. Finally, the deep learning-based SMOTE_ADDA_GO-1D-CNN decreases domain discrepancy with adversarial discriminative domain adaptation. The proposed methodology's efficacy has been explored through F1-Score, which is used as multiclass evaluation metrics, and it has been benchmarked against standard machine learning and deep learning algorithms. The test results of the proposed methodology surpassed all of them with maximum prediction accuracy. Thus, this study contributes to rotor mass imbalance detection and diagnosis for multiclass imbalanced data under varying operational conditions by successfully overcoming potential challenges during fault prediction.

Molybdenum Disulfide-Assisted Spontaneous Formation of Multistacked Gold Nanoparticles for Deep Learning-Integrated Surface-Enhanced Raman Scattering.

Several fabrication methods have been developed for label-free detection in various fields. However, fabricating high-density and highly ordered nanoscale architectures by using soluble processes remains a challenge. Herein, we report a biosensing platform that integrates deep learning with surface-enhanced Raman scattering (SERS), featuring large-area, close-packed three-dimensional (3D) architectures of molybdenum disulfide (MoS2)-assisted gold nanoparticles (AuNPs) for the on-site screening of coronavirus disease (COVID-19) using human tears. Some AuNPs are spontaneously synthesized without a reducing agent because the electrons induced on the semiconductor surface reduce gold ions when the Fermi level of MoS2 and the gold electrolyte reach equilibrium. With the addition of polyvinylpyrrolidone, a two-dimensional large-area MoS2 layer assisted in the formation of close-packed 3D multistacked AuNP structures, resembling electroless plating. This platform, with a convolutional neural network-based deep learning model, achieved outstanding SERS performance at subterascale levels despite the microlevel irradiation power and millisecond-level acquisition time and accurately assessed susceptibility to COVID-19. These results suggest that our platform has the potential for rapid, low-damage, and high-throughput label-free detection of exceedingly low analyte concentrations.

A one-dimensional convolutional neural network-based deep learning approach for predicting cardiovascular diseases

Early detection of cardiovascular diseases (CVDs) is crucial for managing cardiovascular diseases and improving patient outcomes. Deep neural networks have the potential to reduce the reliance on costly and time-consuming clinical tests, leading to cost savings for patients and healthcare systems. This study proposes the development of specialized convolutional neural networks for the automated selection of essential variables, employing various preprocessing procedures. It evaluates the approach using the UCI repository heart disease dataset, focusing on early-stage heart disease identification to enhance early prediction and intervention for CVD. To address the challenge of achieving higher accuracy, we introduce an approach using one-dimensional convolutional neural networks, incorporating extensive testing to optimize the network architecture and enhance predictive performance. Additionally, recognizing the impact of features on accuracy, a comprehensive data analysis was performed. Through a meticulous selection process, we identified and utilized key features that significantly influenced the accuracy of our model, contributing to more reliable predictions. Finally, cross-validation techniques were implemented to precisely evaluate the efficacy of our work. Numerous experiments were conducted to demonstrate the relevance of our research. The prediction accuracy was found to be 99.95% when employing a train–test approach, while it was approximately 98.53% when employing K-Fold cross-validation. In comparison to existing literature, our approach outperforms a recent best study that proposed a Catboost model, achieving an F1-score of about 92.3% and an average accuracy of 90.94%. This signifies a substantial improvement in predictive performance, with a percentage improvement of approximately 9.90% compared to the Catboost model.

A new ViT-Based augmentation framework for wafer map defect classification to enhance the resilience of semiconductor supply chains

Wafer map defect classification plays a crucial role in sustaining the semiconductor supply chain during industrial disruptions by ensuring continuity, resilience, and efficiency while aligning with sustainability principles. Up to 30 percent of production costs is lost to chip testing and yield losses for semiconductor manufacturing supply chain. In the semiconductor practice, wafer map defects recognition plays a linchpin role in the front-end-of-line stage. Defect pattern recognition can directly pinpoint the assignable causes and provide the domain experts with actionable insights. However, various wafer defect types occur differently from each other, which makes the collected wafer map dataset to be highly imbalanced in defect classes. In the face of data imbalance, the classification model usually cannot provide satisfactory classification performance. In this aspect, this paper intends to investigate the wafer defect map classification problem by using Vision Transformer (ViT) as an alternative data augmentation approach. The primary purpose is to alleviate the class-imbalance issue and then the performance of a deep learning-based convolutional neural network for wafer defect classification can be effectively improved. The experimental results demonstrate that the proposed data augmentation method by using ViT proves to be a potential generative model for improving wafer map defect classification, particularly robust on the individual minority class. In a word, the proposed augmentation framework for wafer map defect classification facilitates a more targeted and efficient approach to quality control, resource utilization, and production optimization in maintaining a sustainable semiconductor supply chain, particularly in times of industrial disruption.

Construction of deep learning-based convolutional neural network model for automatic detection of fluid hysteroscopic endometrial micropolyps in infertile women with chronic endometritis

Objective(s)Chronic endometritis (CE) is a localized mucosal inflammatory disorder associated with female infertility of unknown etiology, endometriosis, tubal factors, repeated implantation failure, and recurrent pregnancy loss, along with atypical uterine bleeding and iron deficiency anemia. Diagnosis of CE has traditionally relied on endometrial biopsy and detection of CD138(+) endometrial stromal plasmacytes. To develop a less invasive diagnostic system for CE, we aimed to construct a deep learning-based convolutional neural network (CNN) model for the automatic detection of endometrial micropolyps (EMiP), a fluid hysteroscopy (F-HSC) finding recognized as tiny protrusive lesions that are closely related to this disease. Study designThis is an in silico study using archival images of F-HSC performed at an infertility center in a private clinic. A total of 244 infertile women undergoing F-HSC on the days 6–12 of the menstrual cycle between April 2019 and December 2021 with histopathologically-confirmed CE with the aid of immunohistochemistry for CD138 were utilized. ResultsThe archival F-HSC images of 208 women (78 with EMiP and 130 without EMiP) who met the inclusion criteria were finally subjected to analysis. Following preprocessing of the images, half a set was input into a CNN architecture for training, whereas the remaining images were utilized as the test set to evaluate the performance of the model, which was compared with that of the experienced gynecologists. The sensitivity, specificity, accuracy, precision, and F1-score of the CNN model-aided diagnosis were 93.6 %, 92.3 %, 92.8 %, 88.0 %, and 0.907, respectively. The area under the receiver operating characteristic curves of the CNN model-aided diagnosis (0.930) was at a similar level (p > .05) to the value of conventional diagnosis by three experienced gynecologists (0.927, 0.948, and 0.906). ConclusionThese findings indicate that our deep learning-based CNN is capable of recognizing EMiP in F-HSC images and holds promise for further development of the computer-aided diagnostic system for CE.

Deep learning-based CNN for multiclassification of ocular diseases using transfer learning

ABSTRACT Effective and timely diagnosis and treatment of ocular diseases is essential for swift recovery of the patients. Among ocular diseases, cataract and glaucoma are the most prevalent globally and need adequate attention. The present paper aims to develop an optimised deep learning based convolutional neural network (CNN) for the multi-classification of ocular diseases (normal, glaucoma and cataract). Three pre-trained CNNs (SqueezeNet, Darknet-53, EfficientNet-b0) were optimised concerning batch size (6/8/10) & optimiser type (SGDM, RMSProp, Adam) for obtaining maximum possible accuracy in the detection of multiple ocular diseases (cataract & glaucoma). Darknet-53 (batch size-6, optimiser type-Adam) gave the highest accuracy of 99.4% for a test sample of 1000 images. The performance metrics of Darknet-53 have been computed using a confusion matrix. Confusion matrix is also applied to calculate accuracy, sensitivity, specificity, f1 score and receiver operating curve (ROC). Through comparative performance analysis of the three CNNs, SqueezeNet, Darknet-53 and EfficientNet-b0 achieved the highest accuracy of 95%, 99.4% and 90%, respectively. The results indicate the importance of batch size and optimiser type on the performance of CNN models.

Emotional Recognition Based on Faces through Deep Learning Algorithms

Facial expressions have long been a straightforward way for humans to determine emotions, but computer systems find it significantly more difficult to do the same. Emotion recognition from facial expressions, a subfield of social signal processing, is employed in many different circumstances, but is especially useful for human-computer interaction. Many studies have been conducted on automatic emotion recognition, with the majority utilizing machine learning techniques. However, the identification of basic emotions such as fear, sadness, surprise, anger, happiness, and contempt remains a challenging subject in computer vision. Recently, deep learning has gained more attention as potential solutions for a range of real-world problems, such as emotion recognition. In this work, we refined the convolutional neural network method to discern seven basic emotions and assessed several preprocessing approaches to illustrate their impact on CNN performance. The goal of this research is to enhance facial emotions and features by using emotional recognition. Computers may be able to forecast mental states more accurately and respond with more customised answers if they can identify or recognise the facial expressions that elicit human responses. Consequently, we investigate how a convolutional neural network-based deep learning technique may enhance the recognition of emotions from facial features (CNN). Consequently, we investigate how a convolutional neural network-based deep learning technique may enhance the recognition of emotions from facial features (CNN). Our dataset, which comprises of roughly 32,298 pictures for testing and training, includes multiple face expressions. After noise removal from the input image, the pretraining phase helps reveal face detection, including feature extraction. The preprocessing system helps with this.

Convolutional Neural Network-Based Deep Learning Engine for Mastoidectomy Instrument Recognition and Movement Tracking.

To develop a convolutional neural network-based computer vision model to recognize and track 2 mastoidectomy surgical instruments-the drill and the suction-irrigator-from intraoperative video recordings of mastoidectomies. Technological development and model validation. Academic center. Ten 1-minute videos of mastoidectomies done for cochlear implantation by varying levels of resident surgeons were collected. For each video, containing 900 frames, an open-access computer vision annotation tool was used to annotate the drill and suction-irrigator class images with bounding boxes. A mastoidectomy instrument tracking module, which extracts the center coordinates of bounding boxes, was developed using a feature pyramid network and layered with DETECTRON, an open-access faster-region-based convolutional neural network. Eight videos were used to train the model, and 2 videos were used for testing. Outcome measures included Intersection over Union (IoU) ratio, accuracy, and average precision. For an IoU of 0.5, the mean average precision for the drill was 99% and 86% for the suction-irrigator. The model proved capable of generating maps of drill and suction-irrigator stroke direction and distance for the entirety of each video. This computer vision model can identify and track the drill and suction-irrigator from videos of intraoperative mastoidectomies performed by residents with excellent precision. It can now be employed to retrospectively study objective mastoidectomy measures of expert and resident surgeons, such as drill and suction-irrigator stroke concentration, economy of motion, speed, and coordination, setting the stage for characterization of objective expectations for safe and efficient mastoidectomies.

Abstract 1043: Identification of the novel immune synapse-localized proteome for immuno-oncology using Microscoop-induced targeted photo-biotinylation

Abstract Although tens of immune synapse (IS) protein species are known, there remain many unknown IS-localized protein species. Understanding the proteome of the IS between a target cell and a lymphocyte is crucial for advancing immuno-oncology. The low abundance of ISs and the lack of a definitive enrichment marker have limited efficient proteomic profiling. Here, we used Microscoop™, an innovative system that integrates microscopy, machine learning, and targeted photo-biotinylation to enable precise and spatially specific enrichment of IS proteins, thereby facilitating proteomic discovery of the IS. We utilized Raji B cells as antigen-presenting cells (APCs) and induced IS formation with Jurkat T cells. The system first employed immunofluorescence imaging of CD3, a common IS marker in Jurkat T cells, and used a convolutional neural network-based deep learning algorithm to train the recognition of ISs from CMTPX-stained Raji B cells. Our automated system then successfully achieved spatially-targeted biotin-tagging of proteins at ISs through thousands of fields of view of imaging, deep learning-driven pattern generation, and photochemical labeling. Subsequent streptavidin pull-down and mass spectrometry analysis enabled the identification of IS-specific proteins. Remarkably, our spatial proteomic approach led to the isolation and identification of hundreds of different species at the IS interface, including proteins associated with key components of T-cell receptor (TCR) signaling pathways such as the TCR/CD3 complex, Src and Tec family tyrosine kinases, and pivotal NF-kB signaling proteins. Beyond these, we also identified a significant enrichment of proteins not previously associated with the IS. Our study not only illuminates previously unknown aspects of immune regulation at the IS interface, but also shedded light on proteins potentially important for immuno-oncology. These insights have significant implications for cancer research, particularly in understanding and manipulating immune responses for therapeutic purposes. Citation Format: Weng-Man Chong, Cecile Li, Chun-Kai Huang, Chantal Hoi Yin Cheung, Hsiao-Jen Chang, Chia-Wen Chung, Jung-Chi Liao. Identification of the novel immune synapse-localized proteome for immuno-oncology using Microscoop-induced targeted photo-biotinylation [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 1043.

Pattern Recognition in Embedded Systems for Event Occurrences

Abstract: PR (pattern recognition) typically includes interaction with humans and other complicated processes in the real world, embedded systems are ideal candidates. A typical PR application often considered the more perceptual branch of AI, responds to external events that the system detects through physical sensors or input devices by activating actuators or displaying relevant information. To explore the embedded recognition system and apply the deep learning algorithm to face detection, the deep learning-based Convolutional Neural Network (CNN)suggests two deep face detection methods. These are presented to use the deep learning algorithm.This was done to make it possible for us to use the deep learning algorithm for face detection. Because of this, to analyze the built-in face recognition system and applied the deep learning algorithm to the process of identifying faces. In addition, to do both things simultaneously. OMTCNN's training accuracy is 85.14%, higher than the unimproved algorithm. Accuracy of the recognition and calculation acceleration modules boosts embedded system face detection and identification performance. Embedded deep learning recognition is helpful.

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images

Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.

Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data for such modeling; however, these products often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR modeling using bias-corrected SPPs and the Soil and Water Assessment Tool (SWAT) model for MRB. A convolutional neural network-based deep learning framework was employed to correct biases in four SPPs (TRMM, PERSIANN-CDR, CHIRPS, and CMORPH), resulting in four respective bias-corrected SPPs (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, and ADJ_CMOR). The bias-corrected products were compared against a gauge-based dataset in terms of rainfall analysis, and their performance within the SWAT model was assessed over calibration (2004–2013) and validation (2014–2015). Bias-corrected products demonstrated superior performance in rainfall analysis, with ADJ_TRMM outperforming other products. The SWAT model calibration results showed satisfactory performance across all stations, with a Nash-Sutcliffe Efficiency (NSE) ranging from [0.76–0.87]. Integrating bias-corrected SPPs into the SWAT model significantly increased the RR simulations in the MRB, indicated by higher NSE values [0.72–0.85] compared to uncorrected SPPs [-0.37 to 0.85] at the Kratie station. Besides, the inconsistent performance of bias-corrected products between rainfall analysis and RR modeling was observed, with ADJ_CDR outperforming ADJ_TRMM in the SWAT model. These results highlight the significance of using bias-corrected SPPs in hydrological modeling applications, especially in areas with limited ground-based precipitation data, and highlight the need for further research to refine bias correction methods and address the limitations of the SWAT model.

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.

There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor. In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing. To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco. Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Network anomaly detection and performance evaluation of Convolutional Neural Networks on UNSW-NB15 dataset

The present study uses the benchmark UNSW-NB15 datasets to detect network anomalies using the proposed deep learning-based Convolutional Neural Network (CNN) model. Several studies have already worked on network anomaly detection with some limits. However, earlier research has used old datasets and obtained limited accuracy. In addition, previous studies could not detect the latest malicious activities. Therefore, in the present study, firstly, we implemented the machine learning-based random forest method to diminish the dimensionality of the dataset and pick the most notable features. In this case, the random forest method has set only fifteen features from the forty-one and reduced the complexity of the data. Subsequently, the CNN model was trained and tested on the UNSW-NB15 dataset with 99.00% testing accuracy. The performance of the offered CNN model was assessed using precision, recall and the f1-values. The present study’s outcome can be used in real-time malicious activity detection.

Multi- classification of skin lesions using a deep learning-based convolutional neural network

Skin lesions refer to changes or abnormalities on the skin, such as moles, freckles, cysts, warts, and more. While not all skin lesions are cancerous, some types can indicate or develop into skin cancer. Skin cancer has been on the rise in recent years, causing many deaths. However, manual examination by dermatologists is expensive and time-consuming. Research in this field has focused on developing an automated system to detect skin cancer and reduce the associated mortality rate. However, the performance of such systems needs improvement. In this study, we propose a Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) to augment the data, resulting in a more balanced dataset. Then, we used a Convolutional Neural Network (CNN) model on the benchmark HAM10000 dataset. The dataset comprises seven skin diseases, including Actinic Keratoses, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanocytic nevi, Melanoma, and Vascular skin lesions. With a balanced dataset, the proposed model achieves an accuracy, precision, recall, and f1-score of 99.35%, 99.00%, 99.00%, and 99.00%, respectively. The balanced dataset performs 22% better than an unbalanced dataset.

Efficient Diagnosis of Bacterial Leaf Spot in Tomato Plants using Deep Learning CNN Models

Latest field surveys show that the bacterial spot caused by Xanthomonas campestris pv. vesicatoria (Xcv) has devastated commercial tomato output all over the world. The purpose of this study is to more quickly and accurately diagnose bacterial leaf spot problems in tomato plants. To do this, a deep learning-based Convolutional Neural Network (CNN) named Xantho_Net has been proposed to classify the bacterial and non-bacterial tomato leafs. Various state-of-art pre-trained models i.e., VGG16, MobileNet, DenseNet_201, Inception-ResNet_v2 were selected for comparison purposes. Experiments proved that the proposed CNN model is more accurate than other models with 99% accuracy.