Accurate Disease Classification Research Articles

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.
 Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.
 Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.
 OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.
 METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.
 RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.
 CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Transformer-Based Multi-Modal Data Fusion Method for COPD Classification and Physiological and Biochemical Indicators Identification.

As the number of modalities in biomedical data continues to increase, the significance of multi-modal data becomes evident in capturing complex relationships between biological processes, thereby complementing disease classification. However, the current multi-modal fusion methods for biomedical data require more effective exploitation of intra- and inter-modal interactions, and the application of powerful fusion methods to biomedical data is relatively rare. In this paper, we propose a novel multi-modal data fusion method that addresses these limitations. Our proposed method utilizes a graph neural network and a 3D convolutional network to identify intra-modal relationships. By doing so, we can extract meaningful features from each modality, preserving crucial information. To fuse information from different modalities, we employ the Low-rank Multi-modal Fusion method, which effectively integrates multiple modalities while reducing noise and redundancy. Additionally, our method incorporates the Cross-modal Transformer to automatically learn relationships between different modalities, facilitating enhanced information exchange and representation. We validate the effectiveness of our proposed method using lung CT imaging data and physiological and biochemical data obtained from patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our method demonstrates superior performance compared to various fusion methods and their variants in terms of disease classification accuracy.

Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice.

Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches the highest accuracy rate was obtained using the MobileNetV2 model with a rate of 80.79% accuracy.

GACN: Generative Adversarial Classified Network for Balancing Plant Disease Dataset and Plant Disease Recognition.

Plant diseases are a critical threat to the agricultural sector. Therefore, accurate plant disease classification is important. In recent years, some researchers have used synthetic images of GAN to enhance plant disease recognition accuracy. In this paper, we propose a generative adversarial classified network (GACN) to further improve plant disease recognition accuracy. The GACN comprises a generator, discriminator, and classifier. The proposed model can not only enhance convolutional neural network performance by generating synthetic images to balance plant disease datasets but the GACN classifier can also be directly applied to plant disease recognition tasks. Experimental results on the PlantVillage and AI Challenger 2018 datasets show that the contribution of the proposed method to improve the discriminability of the convolution neural network is greater than that of the label-conditional methods of CGAN, ACGAN, BAGAN, and MFC-GAN. The accuracy of the trained classifier for plant disease recognition is also better than that of the plant disease recognition models studied on public plant disease datasets. In addition, we conducted several experiments to observe the effects of different numbers and resolutions of synthetic images on the discriminability of convolutional neural network.

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields in the face of numerous challenges, including plant diseases. Tomato is a widely cultivated and essential food crop that is particularly susceptible to disease, resulting in significant economic losses and hindrances to food security. Recently, Artificial Intelligence (AI) has emerged as a promising tool for detecting and classifying tomato leaf diseases with exceptional accuracy and efficiency, empowering farmers to take proactive measures to prevent crop damage and production loss. AI algorithms are capable of processing vast amounts of data objectively and without human bias, making them a potent tool for detecting even subtle variations in plant diseases that traditional techniques might miss. This paper provides a comprehensive overview of the most recent advancements in tomato leaf disease classification using Machine Learning (ML) and Deep Learning (DL) techniques, with an emphasis on how these approaches can enhance the accuracy and effectiveness of disease classification. Several ML and DL models, including convolutional neural networks (CNN), are evaluated for tomato leaf disease classification. This review paper highlights the various features and techniques used in data acquisition as well as evaluation metrics employed to assess the performance of these models. Moreover, this paper emphasizes how AI techniques can address the limitations of traditional techniques in tomato leaf disease classification, leading to improved crop yields and more efficient management techniques, ultimately contributing to global food security. This review paper concludes by outlining the limitations of recent research and proposing new research directions in the field of AI-assisted tomato leaf disease classification. These insights will be of significant value to researchers and professionals interested in utilizing ML and DL techniques for tomato leaf disease classification and ultimately contribute to sustainable food production (SDG-3).

An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification.

The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time.

A Deep Learning based Scalable and Adaptive Feature Extraction Framework for Medical Images

Features extraction has a fundamental value in enhancing the scalability and adaptability n of medical image processing framework. The outcome of this stage has a tremendous effect on the reliability of the medical application being developed, particularly disease classification and prediction. The challenging side of features extraction frameworks, in relation to medical images, is influenced by the anatomical and morphological structure of the image which requires a powerful extraction system that highlights high- and low- level features. The complementary of both feature types reinforces the medical image content-based retrieval and allows to access visible structures as well as an in-depth understanding of related deep hidden components. Several existing techniques have been used towards extracting high- and low-level features separately, including Deep Learning based approaches. However, the fusion of these features remains a challenging task. Towards tackling the drawback caused by the lack of features combination and enhancing the reliability of features extraction methods, this paper proposes a new hybrid features extraction framework that focuses on the fusion and optimal selection of high- and low-level features. The scalability and reliability of the proposed method is achieved by the automated adjustment of the final optimal features based on real-time scenarios resulting an accurate and efficient medical images disease classification. The proposed framework has been tested on two different datasets to include BraTS and Retinal sets achieving an accuracy rate of 97% and 98.9%, respectively.

A fatty liver diseases classification network based on adaptive coordination attention with label smoothing

Accurate classification of fatty liver disease (FLD) from fuzzy boundary areas and lesion areas in ultrasound imaging is crucial for clinical diagnosis and treatment. This paper presents an efficient convolutional neural network based on adaptive coordinated attention (ACA), which combines label smoothing and EfficientNet for FLD classification. We only uses pixels and labels of original brightness mode ultrasound images as inputs, and achieves four classification of FLD. It avoids the decoupling of feature extraction and classification, and achieves end-to-end automatic classification identification. Based on ACA, the model can adaptively encode lateral and vertical positional information adaptively into channel attention that adaptively learns information in both spatial directions to enhance the representation of the objects of concern. Furthermore, the interpretability of the model is demonstrated through feature heatmap and hybrid heatmap visualization techniques. The experimental results reveal that the proposed approach attains an accuracy of 94.96% and an AUC of 0.97 on the private dataset. The F1 scores for the four categories are 0.963, 0.942, 0.932, and 0.955, respectively, contributing to an overall F1 score of 0.955, surpassing other baseline models and achieving a new state-of-the-art (SOTA). Meanwhile, this approach also achieves SOTA accuracy of 97.82% on public datasets. The proposed approach has surpassed the manual recognition of fatty liver brightness mode ultrasound images in terms of its cognitive ability, which helps to aid the expert diagnosis. It has better performance and achieves end-to-end classification compared to other methods.

ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction.

Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.

Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

Corn is one of the substitute staple foods in Indonesia after rice. Maize crops grown in Indonesia often experience considerable losses due to maize plant diseases. Generally, plant diseases are initially caused by morphological changes in the leaves. Accurate detection and classification of diseases that appear on the leaves will prevent the widespread spread of the disease. This study will compare classification algorithms, namely Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron to find the best algorithm in the classification of leaf disease in corn plants, namely, cercospora leaf spot gray, common rust, and northern leaf blight using the VGG-16 deep learning model used as image feature extraction. The results showed that the Multilayer Perceptron algorithm produced the best values with accuracy, precision, and recall of 97.4% each.

GastroNet: A robust attention‐based deep learning and cosine similarity feature selection framework for gastrointestinal disease classification from endoscopic images

AbstractDiseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk of inaccurate medical assessments. Computer‐aided Diagnosis systems provide help to doctors for rapid and accurate diagnosis, thus resulting in saving lives. Recently, many techniques are found in the literature that uses deep Convolutional Neural Network (CNN) models for accurate disease classification. However, they have limitations in their ability to detect deformation‐invariant features and lack robustness. The diseased region is highlighted, using attention‐based image generation and superimposition with original images. A lightweight deep CNN model is employed to get significant features. These features are further reduced using a Cosine similarity‐based technique. The proposed framework is assessed using the Kvasir dataset. To verify the effectiveness of the proposed framework, vast experiments are conducted. The overall accuracy of 97.68%, 99.02% precision, 96.37% recall, and an F‐measure of 97.68% are achieved using the 810 significant features. This reduction in features resulted in a significant reduction in classification time. The robustness of the framework can be observed not only in terms of considerable improvement in accuracy, but also in terms of precision as well as recall, and F‐measure.

An Accurate Classification of Rice Diseases Based on ICAI-V4.

Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network's ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network's feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method's strong performance and feasibility for rice disease classification in real-life scenarios.

Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach

In ophthalmology, retinal optical coherence tomography (OCT) images with noticeable structural features help identify human eyes as healthy or diseased. The recently hot artificial intelligence (AI) realized this recognition process automatically. However, speckle noise in the original retinal OCT image reduces the accuracy of disease classification. This study presents a time-saving approach based on deep learning to improve classification accuracy by removing the noise from the original dataset. Firstly, four pre-trained convolutional neural networks (CNNs) from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) were trained to classify the original images into two categories: The noise reduction required (NRR) and the noise-free (NF) images. Among the CNNs, VGG19_BN performed best with 98% accuracy and 99% recall. Then, we used the block-matching and 3D filtering (BM3D) algorithm to denoise the NRR images. Those noise-removed NRR and the NF images form the processed dataset. The quality of images in the dataset is prominently ameliorated after denoising, which is valid to improve the models’ performance. The original and processed datasets were tested on the four pre-trained CNNs to evaluate the effectiveness of our proposed approach. We have compared the CNNs, and the results show the performance of the CNNs trained with the processed dataset is improved by an average of 2.04%, 5.19%, and 5.10% under overall accuracy (OA), Macro F1-score, and Micro F1-score, respectively. Especially for DenseNet161, the OA is improved to 98.14%. Our proposed method demonstrates its effectiveness in improving classification accuracy and opens a new solution to reduce denoising time-consuming for large datasets.

MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model

BackgroundAccurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration.ResultsWe proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification.ConclusionsOur MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.

Extramammary Paget's disease and papillary thyroid carcinoma: two related or unrelated diseases?

Extramammary Paget’s disease (EPD) is a rare tumor occurs mainly in areas with numerous apocrine and or eccrine sweat glands. We report a 49-year-old woman who had referred to another hospital with the complaint of an erythematous and pruritic plaque on the vulva for 6 months. An incisional biopsy had been taken and pathological result was reported as invasive SCC. In her past medical history, there was a history of total thyroidectomy due to follicular variant of papillary thyroid carcinoma two years ago. The patient had been received radioactive iodine for 3 times, however her Iodine isotope scans were still positive. Then she was visited at our hospital for definite treatment. According to the pathologic report she underwent vulvectomy with inguinal lymph node dissection. Histological examination revealed primary invasive Paget disease of the vulva and there was one inguinal lymph node involvement. Second vision of the initial incisional biopsy was also compatible with primary invasive Paget disease of the vulva. We ruled out the possibility of metastatic deposits of thyroid carcinoma in the vulvar lesion and inguinal lymh node by IHC stains. Accurate classification of Paget's disease is essential for appropriate treatment. IHC stains are necessary to differentiate it from other tumors. The possibility of associated malignancy should be considered. To the best of our knowledge this case is the first case of primary Paget disease of the vulva in association with thyroid carcinoma.

Design of ear-contactless stethoscope and improvement in the performance of deep learning based on CNN to classify the heart sound.

Cardiac-related disorders are rapidly growing throughout the world. Accurate classification of cardiovascular diseases is an important research topic in healthcare. During COVID-19, auscultating heart sounds was challenging as health workers and doctors wear protective clothing, and direct contact with patients can spread the outbreak. Thus, contactless auscultation of heart sound is necessary. In this paper, a low-cost ear contactless stethoscope is designed where auscultation is done with the help of a bluetooth-enabled micro speaker instead of an earpiece. The PCG recordings are further compared with other standard electronic stethoscopes like Littman 3M. This work is made to improve the performance of deep learning-based classifiers like recurrent neural networks (RNN) and convolutional neural networks (CNN) for different valvular heart problems using tuning of hyperparameters like learning rate of optimizers, dropout rate, and hidden layer. Hyper-parameter tuning is used to optimize the performances of various deep learning models and their learning curves for real-time analysis. The acoustic, time, and frequency domain features are used in this research. The investigation is made on the heart sounds of normal and diseased patients available from the standard data repository to train the software models. The proposed CNN-based inception network model achieved an accuracy of 99.65 ± 0.06% on the test dataset with a sensitivity of 98.8 ± 0.05% and specificity of 98.2 ± 0.19%. The proposed hybrid CNN-RNN architecture attained 91.17 ± 0.03% accuracy on test data after hyperparameter optimization, whereas the LSTM-based RNN model achieved 82.32 ± 0.11% accuracy. Finally, the evaluated results were compared with machine learning algorithms, and the improved CNN-based Inception Net model is the most effective among others.

Apple Leaf Disease Identification in Complex Background Based on BAM-Net

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation.

An Enhanced Convolutional Neural Network for Accurate Classification of Grape Leaf Diseases

Grape leaf diseases can significantly reduce grape yield and quality, making accurate and efficient identification of these diseases crucial for improving grape production. This study proposes a novel classification method for grape leaf disease images using an improved convolutional neural network. The Xception network serves as the base model, with the original ReLU activation function replaced by Mish to improve classification accuracy. An improved channel attention mechanism is integrated into the network, enabling it to automatically learn important information from each channel, and the fully connected layer is redesigned for optimal classification performance. Experimental results demonstrate that the proposed model (MS-Xception) achieves high accuracy with fewer parameters, achieving a recognition accuracy of 98.61% for grape leaf disease images. Compared to other state-of-the-art models such as ResNet50 and Swim-Transformer, the proposed model shows superior classification performance, providing an efficient method for intelligent diagnosis of grape leaf diseases. The proposed method significantly improves the accuracy and efficiency of grape leaf disease diagnosis and has potential for practical application in the field of grape production.