Convolutional Neural Network Architecture Research Articles

Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.

Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models

Sugarcane is a crucial agricultural crop, providing 75% of the world's sugar production. Like all plant species, any disease that affects sugarcane can significantly impact yield and planning. Traditional manual methods for diagnosing diseases in sugarcane leaves are slow, inefficient, and often lack accuracy. In this study, we present a deep learning-based approach for the robust detection of diseases in sugarcane leaves. Specifically, we trained and evaluated all models from the EfficientNetv1 and EfficientNetv2 architectures, which are among the most notable convolutional neural network (CNN) architectures, using the publicly available Sugarcane Leaf Dataset. This dataset includes 11 disease classes and a total of 6748 images. Additionally, we compared these models with other popular CNN models. Our findings reveal that there is no direct correlation between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 13 models tested, EfficientNet-b6 and InceptionV4 achieved the highest accuracy rates of 93.39% and 93.10%, respectively. These results have a big impact on the ways managers can manage diseases and the agricultural processes of sugarcane production. A deep learning-based disease detection system facilitating the diagnostic process can, in turn, result in a more accurate and faster identification of diseases. That may enable farmers and agricultural managers to make timely and informed decisions, cutting down crop loss and enhancing the whole yield. These highlight the potential of deep learning in developing fast, accurate, and automatic disease diagnosis systems, which can significantly improve disease management and increase the yield of sugarcane.

Multi-Class Classification and Transfer Learning for Rapid Disease Detection in Green Leafy Vegetables Using Convolutional Neural Networks

Plant diseases cause various pathogens, including bacteria, viruses, fungi, and protozoa. Plant diseases and pathogens have an impact on the entire crop and field area. Plant diseases can be identified by various techniques; however, traditional techniques are time-consuming and require more effort. Up-to-date machine learning based approaches are used to identify plant diseases based on the image data. This study investigated the application of convolutional neural network (CNN) models for the detection and classification of diseases affecting green leafy vegetables. Several state-of-the-art CNN architectures, including InceptionV3 and DenseNet121, were evaluated on an image dataset of diseased and healthy leaf samples. Preprocessing techniques such as scaling, cropping, grayscale conversion, and normalization were applied to enhance the input images. The CNN models demonstrated high diagnostic accuracy, with InceptionV3 and DenseNet121 exhibiting exceptional performance across multiple metrics like sensitivity, specificity, accuracy, recall, F-measure, and Matthews correlation coefficient (MCC).

Explainable AI (XAI) Techniques for Convolutional Neural Network-Based Classification of Drilled Holes in Melamine Faced Chipboard

The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-faced chipboard using Explainable AI (XAI) techniques to better understand and interpret Convolutional Neural Network (CNN) models’ decisions. We evaluated three CNN architectures (VGG16, VGG19, and ResNet101) pretrained on the ImageNet dataset and fine-tuned on our dataset of drilled holes. The data consisted of 8526 images, divided into three categories (Green, Yellow, Red) based on the drill’s condition. We used 5-fold cross-validation for model evaluation and applied LIME and Grad-CAM as XAI techniques to interpret the model decisions. The VGG19 model achieved the highest accuracy of 67.03% and the lowest critical error rate among the evaluated models. LIME and Grad-CAM provided complementary insights into the decision-making process of the model, emphasizing the significance of certain features and regions in the images that influenced the classifications. The integration of XAI techniques with CNN models significantly enhances the interpretability and reliability of automated systems for tool condition monitoring in the wood industry. The VGG19 model, combined with LIME and Grad-CAM, offers a robust solution for classifying drilled holes, ensuring better quality control in manufacturing processes.

Novel imbalanced fault diagnosis method based on generative adversarial networks with balancing serial CNN and Transformer (BCTGAN)

In industrial production engineering, machines cannot sustain faults for extended periods, resulting in limited fault data collection. This scarcity of data impedes the advancement of accurate fault diagnosis models, particularly in the context of small-sample, extremely imbalanced fault modeling, which poses a significant and challenging problem. In this paper, we introduce an innovative data augmentation approach for addressing the small-sample imbalanced problem: the balanced conditioning CNN (Convolutional Neural Networks)-Transformer architecture based Generative Adversarial Network (BCTGAN). Firstly, we employ a serial CNN and Transformer architecture as the foundation of the generator. Secondly, we introduce micro-enhancement and SMOTE (Synthetic Minority Oversampling Technique) enhancement of samples to facilitate model training. Micro-enhancement simply augments the sample size, while SMOTE enhancement enhances diversity. Together, these techniques balance the training process by introducing new loss function terms. Finally, outlier samples are mitigated through a designed multi-domain feature screening method. Despite the added time cost and computational complexity, results from three instances demonstrate that the proposed method holds superior diagnostic results with great potential compared to traditional and general GAN-based fault diagnosis methods. Our work provides theoretical and practical insights for modeling the field of small-sample blended disequilibrium fault diagnosis.

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Data augmentation aided excavator activity recognition using deep convolutional conditional generative adversarial networks

Excavator activity recognition is a crucial task with significant practical applications such as assessing production efficiency, injury prevention, and enabling intelligent control. However, implementing deep learning-based activity recognition methods presents considerable challenges due to the requirement of large training datasets. And sensor-based activity recognition demands more convenient and reliable sensors. This paper proposes a generalized data augmentation framework for excavator activity recognition. Main pump pressure and flow signals are generated using Deep Convolutional Conditional Generative Adversarial Networks (DC-CGAN). The proposed framework integrates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for activity recognition. Results indicate that the framework outperforms traditional data augmentation methods regarding the quality of generated data and shallow and single deep networks regarding model accuracy and generalization. Additionally, the framework was implemented on three sizes of excavators and a wheel loader, demonstrating excellent versatility.

A Hybrid Parallel Computing Architecture Based on CNN and Transformer for Music Genre Classification

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed.

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet

Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones on the Figshare data set, with an accuracy level of 0.xxx.

ConjunctiveNet: an improved deep learning-based conjunctive-eyes segmentation and severity detection model

PurposeThe study aims to enhance the detection and classification of conjunctival eye diseases' severity through the development of ConjunctiveNet, an innovative deep learning framework. This model incorporates advanced preprocessing techniques and utilizes a modified Otsu’s method for improved image segmentation, aiming to improve diagnostic accuracy and efficiency in healthcare settings.Design/methodology/approachConjunctiveNet employs a convolutional neural network (CNN) enhanced through transfer learning. The methodology integrates rescaling, normalization, Gaussian blur filtering and contrast-limited adaptive histogram equalization (CLAHE) for preprocessing. The segmentation employs a novel modified Otsu’s method. The framework’s effectiveness is compared against five pretrained CNN architectures including AlexNet, ResNet-50, ResNet-152, VGG-19 and DenseNet-201.FindingsThe study finds that ConjunctiveNet significantly outperforms existing models in accuracy for detecting various severity stages of conjunctival eye conditions. The model demonstrated superior performance in classifying four distinct severity stages – initial, moderate, high, severe and a healthy stage – offering a reliable tool for enhancing screening and diagnosis processes in ophthalmology.Originality/valueConjunctiveNet represents a significant advancement in the automated diagnosis of eye diseases, particularly conjunctivitis. Its originality lies in the integration of modified Otsu’s method for segmentation and its comprehensive preprocessing approach, which collectively enhance its diagnostic capabilities. This framework offers substantial value to the field by improving the accuracy and efficiency of conjunctival disease severity classification, thus aiding in better healthcare delivery.

A Gradient-Based Optimizer Method for Improving the Feature Extraction Capability of Network Architecture and Its Application to Condition Monitoring of Rotating Machinery

Aiming at the problem of inaccurate feature extraction under the massive data of rotating machinery, this paper proposes a feature extraction capability enhancement method based on Gradient-Based Optimizer for trusted network architecture to monitor the conditions of bearings, which firstly transforms the original signal into a wavelet time-frequency image and visualizes the high-dimensional and difficult-to-visualize data in low dimensions. Then, different convolutional neural network architectures are tested by implanting different noise intensities to determine the most robust network architecture. Secondly, this paper adopts the grid method as well as the swarm optimization algorithm to guide the parameters of the model to achieve the best feature extraction effect, compares the optimization effect of several swarm optimization algorithms horizontally, and determines the most effective GBO algorithm among them. Finally, to improve the credibility of the best network model, this paper combines the grad-cam method with the trusted fusion method to improve the accuracy and reliability of the model. Validated by the bearing data of Case Western Reserve University, it shows that the proposed model in this paper has good credibility and accuracy.

Enhanced EEG seizure recognition after hypoxia-ischemia in fetal sheep using transformer-based wavelet-scalogram deep learning

This study introduces transformer-based deep learning models as superior alternatives to convolutional neural network (CNN) architectures for seizure detection after hypoxia–ischemia (HI) in electroencephalography (EEG) recordings in the developing brain. We further demonstrate that these models excel in classifying of varied and subtle morphologies of electrographic seizures observed in the EEG of fetal sheep after HI. The study explores model training combinations from four cohorts of Romney/Suffolk fetal sheep (HI-normothermia-term (n = 7), HI-hypothermia-term (n = 14), sham-normothermia-term (n = 5), and HI-normothermia-preterm (n = 14)), totalling 31,015 EEG segments from 17,300 h of recordings. We evaluated multiple Transformer architectures using both 2D wavelet-scalograms (WS) and 1D raw EEG signals through leave-one-out and k-fold cross-validation, comparing models’ performance with or without sham-normothermia-term data and contrasting them with CNN-based results from our previous work. The Data-Efficient image Transformer (DeiT) emerged as the top-performing model, achieving 99.60 % accuracy (AUC = 0.992) when fed with WS, surpassing the 2D WS CNN result (98.94 % accuracy, AUC = 0.967) from our prior study. Notably, the Wav2Vec2 transformer model, fed with raw 1D EEG segments, demonstrated comparable performance with 99.60 % accuracy (AUC = 0.992), outperforming the 1D CNN result (98.43 % accuracy, AUC = 0.961) from the previous study. More importantly, the transformers can adeptly distinguish between seizures in both the preterm and term brain, and under the influence of treatment with 99.92 % accuracy (AUC = 0.997). Our findings highlight the superior ability of transformer models to capture long-range dependencies within EEG data, enhancing seizure detection without the added complexity of generating 2D WS images. Transformer models show promise for generalized automated seizure detection, irrespective of the perinatal brain maturation stage and the influence of hypothermia, offering potential improvements for seizure detection after HI in newborns.

Celiac Disease Deep Learning Image Classification Using Convolutional Neural Networks.

Celiac disease (CD) is a gluten-sensitive immune-mediated enteropathy. This proof-of-concept study used a convolutional neural network (CNN) to classify hematoxylin and eosin (H&E) CD histological images, normal small intestine control, and non-specified duodenal inflammation (7294, 11,642, and 5966 images, respectively). The trained network classified CD with high performance (accuracy 99.7%, precision 99.6%, recall 99.3%, F1-score 99.5%, and specificity 99.8%). Interestingly, when the same network (already trained for the 3 class images), analyzed duodenal adenocarcinoma (3723 images), the new images were classified as duodenal inflammation in 63.65%, small intestine control in 34.73%, and CD in 1.61% of the cases; and when the network was retrained using the 4 histological subtypes, the performance was above 99% for CD and 97% for adenocarcinoma. Finally, the model added 13,043 images of Crohn's disease to include other inflammatory bowel diseases; a comparison between different CNN architectures was performed, and the gradient-weighted class activation mapping (Grad-CAM) technique was used to understand why the deep learning network made its classification decisions. In conclusion, the CNN-based deep neural system classified 5 diagnoses with high performance. Narrow artificial intelligence (AI) is designed to perform tasks that typically require human intelligence, but it operates within limited constraints and is task-specific.

A Novel Model Based on CNN-ViT Fusion and Ensemble Learning for the Automatic Detection of Pes Planus.

Background: Pes planus, commonly known as flatfoot, is a condition in which the medial arch of the foot is abnormally low or absent, leading to the inner part of the foot having less curvature than normal. Symptom recognition and errors in diagnosis are problems encountered in daily practice. Therefore, it is important to improve how a diagnosis is made. With the availability of large datasets, deep neural networks have shown promising capabilities in recognizing foot structures and accurately identifying pes planus. Methods: In this study, we developed a novel fusion model by combining the Vgg16 convolutional neural network (CNN) model with the vision transformer ViT-B/16 to enhance the detection of pes planus. This fusion model leverages the strengths of both the CNN and ViT architectures, resulting in improved performance compared to that in reports in the literature. Additionally, ensemble learning techniques were employed to ensure the robustness of the model. Results: Through a 10-fold cross-validation, the model demonstrated high sensitivity, specificity, and F1 score values of 97.4%, 96.4%, and 96.8%, respectively. These results highlight the effectiveness of the proposed model in quickly and accurately diagnosing pes planus, making it suitable for deployment in clinics or healthcare centers. Conclusions: By facilitating early diagnosis, the model can contribute to the better management of treatment processes, ultimately leading to an improved quality of life for patients.

Classification of brain tumor using deep learning at early stage

Early detection and classification of brain tumors are crucial for patient survival. This study proposes a comprehensive deep learning approach for early brain tumor classification using medical imaging data. A diverse dataset encompassing various tumor types, stages, and healthy brain images is utilized. Preprocessing techniques like augmentation and normalization enhance data robustness. A convolutional neural network (CNN) architecture serves as the primary model, leveraging transfer learning from pre-trained models to extract relevant features even with limited data. The training process optimizes hyperparameters to prevent overfitting, and performance is evaluated using metrics like accuracy, precision, recall, F1 score, confusion matrices, and ROC curves on a separate test set. Focusing on early detection, the model explores predicting tumor growth trajectories and identifying subtle pre-tumor patterns, aligning with expert diagnoses and boosting real-world applicability. Ethical and regulatory guidelines are adhered to in data handling. Continuous improvement involves updating the model with new data and monitoring its clinical performance. This research contributes to advancing early tumor classification methods, potentially improving patient outcomes and treatment strategies.

FwSVM-Net: A novel deep learning-based automatic building extraction from aerial images

The building layer, one of the fundamental elements of mapping, features various layouts and architectures and is used in many mapping activities, such as urban planning, property management, illegal building detection, and disaster investigation. Therefore, accessing building information from remotely sensed images in a fast and automated manner is a popular topic. This paper aims to design a new Convolutional Neural Network (CNN) architecture called FwSVM-Net (Fast with Support Vector Machine Network) for building extraction. The FwSVM-Net, with 16 convolutional layers, employs a fusion mechanism to reduce the semantic gap between the encoder and decoder sections. In the architecture's classification layer, a Support Vector Machine (SVM) is used to increase segmentation accuracy while reducing parameter density. The Massachusetts Building Dataset was used for the automatic building extraction from aerial images. The performance of the FwSVM-Net on the test data was calculated at 97 %, 91 %, 87 %, 89 %, and 86 % for Overall Accuracy (OA), Precision (Pr), Recall (Rc), F1-Score (F1), and Intersection over Union (IoU), respectively. FwSVM-Net shows approximately ±2 % similarity in accuracy with the U-Net architecture, which is trained and evaluated with the same dataset. Despite this, the fact that the FwSVM-Net completed the training process approximately twice as fast as the U-Net architecture provides a significant time advantage. As a result, it is predicted that the FwSVM-Net will offer speed and convenience in terms of usability because of its performance, which is equivalent to the performance of the U-Net architecture for segmentation, and its time advantage.

Deep learning implementation using convolutional neural network in inorganic packaging waste sorting

Municipal solid waste is a significant issue that causes environmental contamination. One of the most prevalent wastes that is difficult to decompose is waste from inorganic packaging. Inorganic packaging waste management can be done by sorting waste as the first step before going through subsequent processing. However, waste sorting is currently still difficult to do by human power in waste management facilities, so it is necessary to design a system that can assist the waste sorting process. This research aims to develop a model that can classify inorganic packaging at waste processing sites. To develop the model, we used five pre-trained Convolutional Neural Network (CNN) architectures, namely Xception, Inception V3, ResNet-50, Resnet-50 V2, and DenseNet-201. Then, the best architecture based on some metric performances will be tuned. The result displayed that the CNN model with Densenet 201 architecture, accompanied by tuning, achieved the best performance to classify the waste. The accuracy for the validation dataset is 95.31 %, the accuracy for the testing dataset is 95.6 %, precision is 0.96, recall is 0.96, and the F1-score is 0.96. The results of those performance metrics show that the model can predict the image of inorganic packaging waste well for further application to an automated waste sorting system.

Convolutional Neural Network Processing of Radio Emission for Nuclear Composition Classification of Ultra-High-Energy Cosmic Rays

Ultra-high-energy cosmic rays (UHECRs) are extremely rare energetic particles of ordinary matter in the Universe, traveling astronomical distances before reaching the Earth’s atmosphere. When primary cosmic rays interact with atmospheric nuclei, cascading extensive air showers (EASs) of secondary elementary particles are developed. Radio detectors have proven to be a reliable method for reconstructing the properties of EASs, such as the shower’s axis, its energy, and its maximum (Xmax). This aids in understanding fundamental astrophysical phenomena, like active galactic nuclei and gamma-ray bursts. Concurrently, data science has become indispensable in UHECR research. By applying statistical, computational, and deep learning methods to both real-world and simulated radio data, researchers can extract insights and make predictions. We introduce a convolutional neural network (CNN) architecture designed to classify simulated air shower events as either being generated by protons or by iron nuclei. The classification achieved a stable test error of 10%, with Accuracy and F1 scores of 0.9 and an MCC of 0.8. These metrics indicate strong prediction capability for UHECR’s nuclear composition, based on data that can be gathered by detectors at the world’s largest cosmic rays experiment on Earth, the Pierre Auger Observatory, which includes radio antennas, water Cherenkov detectors, and fluorescence telescopes.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.

Comparative Analysis of CNN Algorithms for Mushroom Classification with Proposed Lightweight CNN Model

The classification of mushroom species presents significant ecologic and health-related challenges; advancement in classification techniques is required to gain reliable identifications. This study aims to explain a methodology that was devised and evaluated in the development of a novel, lightweight Convolutional Neural Network (CNN) designed specifically for the task of mushroom classification. The paper provides a custom CNN model that is computationally cost-effective and capable of high-precision classification, fit for real-time usage. Hence, the proposed model was evaluated on this dataset of curated mushroom images with traditional classifiers and state-of-the-art CNN architectures, such as EfficientNet-B7, ResNet50, InceptionV3, and MobileNetV2. The custom model is depth-wise separations engineered in such a way that while they reduce the computational load, they don't compromise the effectiveness of the model. The custom model achieved a test score of 0.68, which is moderate compared to more established models such as EfficientNet-B7 or ResNet50. This approach helps the model function effectively even on platforms having low computational resources. A comprehensive evaluation reveals that a custom CNN has reasonable accuracy in the identification of different mushroom species vis-à-vis existing models, but also significantly lightens the classification process.