Feature Extraction Process Research Articles

Emotion Based Age and Gender Detection Using ML

-In recent years, emotion-based age and gender detection using machine learning has gained significant attention due to its potential applications in various domains, including marketing, security, and human-computer interaction. This study explores the effectiveness of convolutional neural networks (CNNs) in accurately classifying age and gender based on facial expressions. Specifically, we leverage advanced architectures such as VGG16 and ResNet, which are renowned for their deep learning capabilities. By utilizing transfer learning techniques, we enhance the feature extraction process from facial images, thereby improving the model's performance on age and gender classification tasks.Our experiments demonstrate that integrating emotional analysis with demographic detection can yield more personalized and context-aware interactions.we present a comparative analysis of the performance of VGG16 and ResNet models in this domain. The results indicate that while both models achieve commendable accuracy, ResNet's residual connections significantly mitigate the vanishing gradient problem, leading to superior performance in recognizing nuanced emotional expressions across different age groups and genders. Additionally, we discuss the implications of these findings in real-world applications, emphasizing the importance of ethical considerations when deploying emotion detection systems. This study contributes to the growing body of research in affective computing and provides a foundation for future advancements in emotion-based demographic detection technologies. Keywords: Emotion detection, Age classification, Gender classification, Machine learning, Convolutional neural networks, VGG16, ResNet, Transfer learning, Affective computing

Enhanced Deep Learning Approaches for Text Classification: A Comprehensive Review

Deep learning models have outperformed traditional machine learning approaches in a variety of text classification tasks, such as sentiment analysis, news categorization, question answering, and natural language inference. Text classification is a widely employed technique in natural language processing, encompassing applications like spam detection, news categorization, information retrieval, sentiment analysis, and intent assessment. Conventional text classifiers utilizing machine learning methods suffer from issues like sparse data, dimensionality challenges, and limited generalization capabilities. In contrast, deep learning-based classifiers address these shortcomings, eliminating the need for intricate feature extraction processes while offering enhanced learning capabilities and increased prediction accuracy. For instance, convolutional neural networks (CNNs). This article outlines the text classification process, with a specific focus on the deep learning models utilized in the context of text classification.

Efficient Tomato Disease Detection Using MaxMin-Diffusion Mechanism and Lightweight Techniques

This paper proposes a disease detection model based on the maxmin-diffusion mechanism, aimed at improving the accuracy and robustness of disease detection tasks in the agricultural field. With the development of smart agriculture, automated disease detection has become one of the key tasks driving agricultural modernization. Traditional disease detection models often suffer from significant accuracy loss and robustness issues when dealing with complex disease types and dynamically changing time-series data. To address these problems, this paper introduces the maxmin-diffusion mechanism, which dynamically adjusts attention weights to enhance the model’s focus on key disease regions while suppressing interference from irrelevant areas, significantly improving the segmentation accuracy of disease regions. Through a series of experiments, the proposed model demonstrates outstanding performance across various disease detection tasks. For bacterial spot disease detection, the model achieves a precision of 0.98, recall of 0.95, accuracy of 0.96, and mIoU of 0.96, indicating that it can efficiently and accurately identify disease regions even in complex backgrounds. Compared to traditional self-attention and CBAM mechanisms, the maxmin-diffusion mechanism shows significant advantages in fine-grained feature extraction and time-series data processing, particularly in the recognition of dynamically changing disease regions, where it exhibits higher detection accuracy and robustness. Furthermore, the model underwent lightweight optimization, enabling the proposed disease detection model to not only achieve high-precision detection but also run efficiently on resource-constrained mobile devices. This provides strong technical support for the application of smart agriculture.

The Application Value of an Artificial Intelligence-Driven Intestinal Image Recognition Model to Evaluate Intestinal Preparation before Colonoscopy.

Aims/Background Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. Methods In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023. Among them, 47 cases were evaluated based on the intestinal preparation map and the last fecal characteristics (Regular group), and 51 cases were evaluated using an AI-driven intestinal image recognition model (AI group). The duration of colonoscopy examination, intestinal cleanliness, incidence of adverse reactions, and satisfaction with intestinal preparation of the two groups were analyzed. Results The time for colonoscopy in the AI group was shorter than that in the Regular group, and the intestinal cleanliness score in the AI group was higher than that in the Regular group (p < 0.05). The incidence of adverse reactions in the AI group (3.92%) was lower than that in the Regular group (10.64%), but the difference was not statistically significant (p > 0.05). The satisfaction rate of intestinal preparation in the AI group (96.08%) was comparable to that of the Regular group (82.98%) (p > 0.05). Conclusion Compared with the assessment based solely on the intestinal preparation map and the last fecal characteristics, the application of AI intestinal image recognition model in intestinal preparation before colonoscopy can shorten the time of colonoscopy and improve intestinal cleanliness, but with comparable patient satisfaction and safety.

Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition

Batik as a cultural heritage is one of the heritages that needs to be preserved so that it continues to be recognized from generation to generation. Efforts to preserve batik can be made by using technology that can recognize batik motifs. Pattern recognition is a branch of science related to the identification, classification, and interpretation of patterns. Deep learning is one of the technologies that can be used very well for pattern recognition, especially for syllable and image recognition. Convolutional neural network (CNN) is one of the most popular deep learning methods and the most established algorithm for deep learning models. The main advantage of CNN over the preceding methods is its ability to automatically detect features, making the feature extraction and classification process highly organized. This study aims to apply CNN for batik pattern recognition. The batik patterns used were geometric patterns, divided into 7 batik classes. Experiments were conducted on 3100 data, consisting of 3000 for training set and 100 for testing set. At the preprocessing stage, the batik image was resized to 28x28, and the color was changed to grayscale. Training was carried out on 100, 200, and 300 epochs. The classification results prove that CNN can recognize batik patterns well with an accuracy rate of 95%.

Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.

Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion. To overcome these challenges, we propose an end-to-end sequence-based model called BTDHDTA. In the feature extraction process, the bidirectional gated recurrent unit (GRU), transformer encoder, and dilated convolution are employed to extract global, local, and their correlation patterns of drug and target input. Additionally, a module combining convolutional neural networks with a Highway connection is introduced to fuse drug and protein deep features. We evaluate the performance of BTDHDTA on three benchmark data sets (Davis, KIBA, and Metz), demonstrating its superiority over several current state-of-the-art methods in key metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the mean (R m 2). The results indicate that our method achieves a better performance in DTA prediction. In the case study, we use the BTDHDTA model to predict the binding affinities between 3137 FDA-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins, validating the model's effectiveness in practical scenarios.

Synchronized Multi-Augmentation with Multi-Backbone Ensembling for Enhancing Deep Learning Performance

This study introduces a novel technique called Synchronized Multi-Augmentation (SMA) combined with multi-backbone (MB) ensembling to enhance model performance and generalization in deep learning (DL) tasks in real-world scenarios. SMA utilizes synchronously augmented input data for training across multiple backbones, improving the overall feature extraction process. The outputs from these backbones are fused using two distinct strategies: the averaging fusion method, which averages predictions, and the dense fusion method, which averages features through a fully connected network. These methods aim to boost accuracy and reduce computational costs, particularly in Edge Intelligence (EI) systems with limited resources. The proposed SMA technique was evaluated on the CIFAR-10 dataset, highlighting its potential to enhance classification tasks in DL workflows. This study provides a comprehensive analysis of various backbones, their ensemble methods, and the impact of different SMAs on model performance. The results demonstrate that SMAs involving color adjustments, such as contrast and equalization, significantly improve generalization under varied lighting conditions that simulated real-world low-illumination conditions, outperforming traditional spatial augmentations. This approach is particularly beneficial for EI hardware, such as microcontrollers and IoT devices, which operate under strict constraints like limited processing power and memory and real-time processing requirements. This study’s findings suggest that employing SMA and MB ensembling can offer significant improvements in accuracy, generalization, and efficiency, making it a viable solution for deploying DL models on edge devices with constrained resources under real-world practical conditions.

A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.

Breast cancer remains a significant global health concern and is a leading cause of mortality among women. The accuracy of breast cancer diagnosis can be greatly improved with the assistance of automatic segmentation of breast ultrasound images. Research has demonstrated the effectiveness of convolutional neural networks (CNNs) and transformers in segmenting these images. Some studies combine transformers and CNNs, using the transformer's ability to exploit long-distance dependencies to address the limitations inherent in convolutional neural networks. Many of these studies face limitations due to the forced integration of transformer blocks into CNN architectures. This approach often leads to inconsistencies in the feature extraction process, ultimately resulting in suboptimal performance for the complex task of medical image segmentation. This paper presents CSAU-Net, a cross-scale attention-guided U-Net, which is a combined CNN-transformer structure that leverages the local detail depiction of CNNs and the ability of transformers to handle long-distance dependencies. To integrate global context data, we propose a cross-scale cross-attention transformer block that is embedded within the skip connections of the U-shaped architectural network. To further enhance the effectiveness of the segmentation process, we incorporated a gated dilated convolution (GDC) module and a lightweight channel self-attention transformer (LCAT) on the encoder side. Extensive experiments conducted on three open-source datasets demonstrate that our CSAU-Net surpasses state-of-the-art techniques in segmenting ultrasound breast lesions.

A Computationally Efficient Method for the Diagnosis of Defects in Rolling Bearings Based on Linear Predictive Coding

Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis.

Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm.

The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture's left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats.

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.

Cancer's heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques address these challenges by improving the interpretability and performance of machine learning (ML) models in high-dimensional datasets. Feature selection methods-such as filter, wrapper, and embedded techniques-play a critical role in enhancing the precision of cancer diagnostics by identifying relevant biomarkers. The integration of multi-omics data and ML algorithms facilitates a more comprehensive understanding of tumor heterogeneity, advancing both diagnostics and personalized therapies. However, challenges such as ensuring data quality, mitigating overfitting, and addressing scalability remain critical limitations of these methods. Artificial intelligence (AI)-powered feature selection offers promising solutions to these issues by automating and refining the feature extraction process. This review highlights the transformative potential of these approaches while emphasizing future directions, including the incorporation of deep learning (DL) models and integrative multi-omics strategies for more robust and reproducible findings.

Pendekatan Hybrid Untuk Deteksi Tepi Citra Menggunakan Algoritma Pembelajaran Mendalam DAM Pemrosesan Berbasis Morfologi

In the digital era, image edge detection plays an important role in various image processing applications such as medical analysis and object recognition. This research develops a hybrid model that combines the high accuracy of deep learning with the efficiency and robustness to noise of morphological processing to improve edge detection performance. The method used involves deep learning models for image feature extraction and morphological processing techniques to improve detection results. The research results show that this hybrid model is able to overcome the weaknesses of traditional methods and pure deep learning by producing edge detection that is smoother and more continuous, and more efficient in processing time. Tests on the BSDS500 dataset and medical images show that this model provides consistent performance in a variety of image conditions, including images with high noise and low contrast. This hybrid approach offers a practical and effective solution for a variety of image processing applications.

Intelligent Remote Sensing Scene Classification Model for On-Board Training of Resource-Constrained Devices

Remote Sensing Scene Classification (RSSC) is the distinctive classification of remote sensing images into numerous classes of scene classifications based on the image content. RSSC plays a significant role in several domains, like land mapping, agriculture, and the classification of disaster-prone regions. The Internet of Things (IoT) is a dynamic global network of devices, for example, vehicles, sensors, actuators, surveillance cameras, etc. These interconnected objects were distinctively recognizable and they could separately transfer and obtain valuable data through the network. However, satellite images were frequently degraded and blurred owing to aerosol dispersion under haze, fog, and other weather circumstances, decreasing the color fidelity and contrast of the image. To use effectual RSSC in real-time, widespread researchers concentrate on creating aerospace image processing systems, like airborne or spaceborne systems. Recently, with the quick improvement of deep learning (DL) and Machine learning (ML) techniques, the performance of RSSC has significantly developed owing to the hierarchical feature representation learning. Both technique has greater achievement in the domain of image scene classification. This study presents a Leveraging Tiny Convolutional Neural Networks with a Water Cycle Algorithm for Remote Sensing Scene Classification (LTCNN-WCRSSC) model. The LTCNN-WCRSSC technique is designed for efficient RSS classification in resource-constrained devices with on-board training capabilities. At first, the LTCNN-WCRSSC model applies image processing using a median filter (MF) to eliminate the noise. Next, the feature extraction process can be exploited by the ConvNeXt-Tiny method. For the RSSC model, the spatiotemporal attention bidirectional long short-term memory (STA-BiLSTM) technique is performed. Eventually, the water cycle algorithm (WCA)-based hyperparameter choice process can be performed to optimize the classification results of the STA-BiLSTM algorithm. The experimental evaluation of the LTCNN-WCRSSC technique takes place using a benchmark image dataset. The stimulated results indicated the superior performances of the LTCNN-WCRSSC model over other approaches.

Unsupervised Dual Deep Hashing with Semantic-Index and Content-Code for Cross-Modal Retrieval.

Hashing technology has exhibited great cross-modal retrieval potential due to its appealing retrieval efficiency and storage effectiveness. Most current supervised cross-modal retrieval methods heavily rely on accurate semantic supervision, which is intractable for annotations with ever-growing sample sizes. By comparison, the existing unsupervised methods rely on accurate sample similarity preservation strategies with intensive computational costs to compensate for the lack of semantic guidance, which causes these methods to lose the power to bridge the semantic gap. Furthermore, both kinds of approaches need to search for the nearest samples among all samples in a large search space, whose process is laborious. To address these issues, this paper proposes an unsupervised dual deep hashing (UDDH) method with semantic-index and content-code for cross-modal retrieval. Deep hashing networks are utilized to extract deep features and jointly encode the dual hashing codes in a collaborative manner with a common semantic index and modality content codes to simultaneously bridge the semantic and heterogeneous gaps for cross-modal retrieval. The dual deep hashing architecture, comprising the head code on semantic index and tail codes on modality content, enhances the efficiency for cross-modal retrieval. A query sample only needs to search for the retrieved samples with the same semantic index, thus greatly shrinking the search space and achieving superior retrieval efficiency. UDDH integrates the learning processes of deep feature extraction, binary optimization, common semantic index, and modality content code within a unified model, allowing for collaborative optimization to enhance the overall performance. Extensive experiments are conducted to demonstrate the retrieval superiority of the proposed approach over the state-of-the-art baselines.

Computer Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with Explainable Artificial Intelligence Approach

Brain tumors (BT) are a difficult and dangerous medical condition, and the accurate and early analysis of these tumors is crucial for suitable treatment. Explainability in clinical image diagnosis role a vital play in the correct analysis and treatment of tumors that supports medical staff's optimum understanding of the image analysis performances rely upon deep methods. Artificial intelligence (AI), in certain deep neural networks (DNNs) has attained remarkable outcomes for clinical image analysis in many applications. However, the need for explainability of deep neural approaches has been assumed that major restriction before executing these approaches in medical practice. Explainable AI, or XAI, is a vital module in this context as it supports medical staff and patients in understanding the AI's decision-making model, enhancing trust and transparency. It leads to optimum patient care and performance but making sure that medical staff can make learned decisions depends on AI-driven insights. Therefore, this study develops a novel Computer-Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with an Explainable Artificial Intelligence (CABTD-COAXAI) approach. The purpose of the CABTD-COAXAI technique is to exploit XAI and hyperparameter-tuned deep learning (DL) approaches for automated BT analysis. To accomplish this, the CABTD-COAXAI technique follows a Gaussian filtering (GF) based noise removal process. Besides, the CABTD-COAXAI technique utilizes the EfficientNetB7 methods for the feature extraction process. Additionally, the hyperparameter tuning of the EfficientNetB7 method is performed by the use of COA. Furthermore, the classification of the BT process can be performed by the usage of a convolutional autoencoder (CAE). Finally, the CABTD-COAXAI system combines the XAI method named LIME to effectively understand and explainability of the black-box model for automated BT diagnosis. The simulation result of the CABTD-COAXAI technique has been tested on a benchmark BT database. The extensive outcomes inferred that the CABTD-COAXAI method reaches superior performance over other models in terms of different measures

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.

Metaheuristic-assisted deep learning model for fake news detection

ABSTRACT Spreading fake news on social media is regarded as a cybercrime that greatly affects society, government and people. Identifying this fake news by manual analysis is a complex task. Hence, various researchers carried out an analysis on exploiting intelligent approaches to detect fake news. This work aims to propose a hybrid deep learning model for fake news detection. In these works, the news is verified in a unidirectional manner. Hence, there is a demand to change the present scenario and a new model is required for increasing the accuracy of the fake news detection. In this work, the deep learning (DL) model Long Short Term Memory (LSTM) with 3 Parallel-concatenated Convolutional Neural Networks (PCCNN) is utilised for fake news detection. For the feature extraction process, the methods like Term Frequency- Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) are utilised. Finally, for optimising the weights of the neural network, the metaheuristic optimisation Enhanced Sun Flower Optimisation (ESFO) algorithm is used. The robustness of LSTM-PCCNN is compared with other deep learning models to verify its robustness. The experimentation is carried out on the two benchmark datasets and obtained better accuracies of 0.994 on the fake news and 0.997 on the Real and fake news datasets respectively.

Lightweight remote sensing image super-resolution with sequential multi-scale feature enhancement network

ABSTRACT Remote sensing images possess abundant texture features and significant autocorrelation. However, the extensive network parameters and high computational demands of current super-resolution (SR) methods make them challenging to implement on mobile devices. This work proposes a lightweight model named the Sequential Multi-Scale Feature Enhancement Network (SMFEN) that address the issue on single remote sensing image super-resolution. Our sequential structure allows for a larger receptive field (RF) with minimal parameters, which can gradually build complex high-level multi-scale feature representations from simple low-level features, realizing the feature extraction process from concrete to abstract. In addition, we design a high-frequency multi-scale attention block which use multi-scale high-frequency details to facilitate the fusion of contextual information across different scales and effectively recover texture and edge information. Comprehensive experimental results demonstrate that our SMFEN network outperforms the latest lightweight SR methods.

A mutual inclusion mechanism for precise boundary segmentation in medical images.

Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process. We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation. The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

Classifying Alzheimer's Disease Using a Finite Basis PhysicsNeural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD-FBPINN) is proposed. Initially, images are collected from AD Neuroimaging Initiative (ADNI) dataset. The images are fed to Pre-processing segment. During the preprocessing phase the reverse lognormal Kalman filter (RLKF) is used to enhance the input images. Then the preprocessed images are given to the feature extraction process. Feature extraction is done by Newton-time-extracting wavelet transform (NTEWT), which is used to extract the statistical features such as the mean, kurtosis, and skewness. Finally the features extracted are given to FBPINNs for Classifying AD such as early mild cognitive impairment (EMCI), AD, mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), normal control (NC), and subjective memory complaints (SMCs). In General, FBPINN does not express adapting optimization strategies to determine optimal factors to ensure correct AD classification. Hence, sea-horse optimization algorithm (SHOA) to optimize FBPINN, which accurately classifies AD. The proposed technique implemented in python and efficacy of the CAD-FBPINN technique is assessed with support of numerous performances like accuracy, precision, Recall, F1-score, specificity and negative predictive value (NPV) is analyzed. Proposed CAD-FBPINN method attain 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; 20.53%, 25.34%, and 29.64% higher NP values analyzed with the existing for Classifying AD Stages through Brain Modifications using FBPINNs Optimized with sea-horse optimizer. Then, the effectiveness of the CAD-FBPINN technique is compared to other methods that are currently in use, such as AD diagnosis and classification using a convolution neural network algorithm (DC-AD-AlexNet), Predicting diagnosis 4 years before Alzheimer's disease incident (PDP-ADI-GCNN), and Using the DC-AD-AlexNet convolution neural network algorithm, diagnose and classify AD.