An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

Diagnostics, Journal Year: 2022, Volume and Issue: 12(9), P. 2115 - 2115

Published: Aug. 31, 2022

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today's medical practices, time-consuming procedure that may lead to patient's death later stages. The diagnosis of at an earlier stage crucial for success rate complete cure. efficient task. Therefore, numbers skilful dermatologists around globe are not enough deal with healthcare. huge difference between data from various sector classes leads imbalance problems. Due issues, deep learning models often trained on one class more than others. This study proposes novel learning-based detector imbalanced dataset. Data augmentation was used balance overcome imbalance. Skin Cancer MNIST: HAM10000 dataset employed, which consists seven lesions. Deep widely disease through images. (AlexNet, InceptionV3, and RegNetY-320) were employed classify cancer. proposed framework also tuned combinations hyperparameters. results show RegNetY-320 outperformed InceptionV3 AlexNet terms accuracy, F1-score, receiver operating characteristic (ROC) curve both balanced datasets. performance better conventional methods. ROC value obtained 91%, 88.1%, 0.95, significantly those state-of-the-art method, achieved 85%, 69.3%, 0.90, respectively. Our assist identification, could save lives, reduce unnecessary biopsies, costs patients, dermatologists, professionals.

Language: Английский

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An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Diagnostics, Journal Year: 2022, Volume and Issue: 12(7), P. 1531 - 1531

Published: June 23, 2022

Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on psychological physiological development sufferers their carers, as well economic social development. Attributed to shortage medical staff, automatic diagnosis AD has become more important relieve workload staff increase accuracy diagnoses. Using MRI scans inputs, an detection model been designed using convolutional neural network (CNN). To enhance fine-tuning hyperparameters and, thus, accuracy, transfer learning (TL) introduced, which brings domain knowledge from heterogeneous datasets. Generative adversarial (GAN) applied generate additional training data in minority classes benchmark Performance evaluation analysis three (OASIS-series) datasets revealed effectiveness proposed method, increases by 2.85−3.88%, 2.43−2.66%, 1.8−40.1% ablation study GAN TL, comparison with existing works, respectively.

Language: Английский

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Development of Deep Learning Methodology for Maize Seed Variety Recognition Based on Improved Swin Transformer

Agronomy, Journal Year: 2022, Volume and Issue: 12(8), P. 1843 - 1843

Published: Aug. 4, 2022

In order to solve the problems of high subjectivity, frequent error occurrence and easy damage traditional corn seed identification methods, this paper combines deep learning with machine vision utilization basis Swin Transformer improve maize recognition. The study was focused on feature attention multi-scale fusion learning. Firstly, input image into network obtain shallow features features; secondly, a layer introduced give weights different stages strengthen suppress; finally, were fused construct images, images are divided 19 varieties through classifier. experimental results showed that average precision, recall F1 values MFSwin model test set 96.53%, 96.46%, 96.47%, respectively, parameter memory is 12.83 M. Compared other models, achieved highest classification accuracy results. Therefore, neural proposed in can classify seeds accurately efficiently, could meet high-precision requirements provide reference tool for identification.

Language: Английский

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Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review

Processes, Journal Year: 2023, Volume and Issue: 11(6), P. 1720 - 1720

Published: June 4, 2023

Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. includes unsupervised and supervised learning, data pre-processing, feature engineering, selection, assessment, methods. Various problems processing could be resolved using these techniques. is increasingly being used in industry improve production efficiency, reduce waste, create personalized customer experiences. may ingredient utilization save costs, automate operations such as packing labeling, even forecast consumer preferences develop products. also identify safety hazards before they reach consumer, contaminants or spoiled food. The usage of machine sector predicted rise near future more businesses understand potential this technology enhance experience boost productivity. utilized nano-technological fruit vegetable preservation. algorithms find trends regarding various factors that impact quality product preserved examining from prior tests. Furthermore, determine parameter combinations result maximal produce review discusses relevance ready-to-eat foods its use tool preservation were highlighted. application agriculture, packaging, processing, reviewed. working principle methodology, well principles discussed.

Language: Английский

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EfficientRMT-Net—An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases

Sensors, Journal Year: 2023, Volume and Issue: 23(23), P. 9516 - 9516

Published: Nov. 30, 2023

The primary objective of this study is to develop an advanced, automated system for the early detection and classification leaf diseases in potato plants, which are among most cultivated vegetable crops worldwide. These diseases, notably late blight caused by Alternaria solani Phytophthora infestans, significantly impact quantity quality global production. We hypothesize that integration Vision Transformer (ViT) ResNet-50 architectures a new model, named EfficientRMT-Net, can effectively accurately identify various diseases. This approach aims overcome limitations traditional methods, often labor-intensive, time-consuming, prone inaccuracies due unpredictability disease presentation. EfficientRMT-Net leverages CNN model distinct feature extraction employs depth-wise convolution (DWC) reduce computational demands. A stage block structure also incorporated improve scalability sensitive area detection, enhancing transferability across different datasets. tasks performed using average pooling layer fully connected layer. was trained, validated, tested on custom datasets specifically curated detection. EfficientRMT-Net's performance compared with other deep learning transfer techniques establish its efficacy. Preliminary results show achieves accuracy 97.65% general image dataset 99.12% specialized Potato dataset, outperforming existing methods. demonstrates high level proficiency correctly classifying identifying even cases distorted samples. provides efficient accurate solution plant potentially enabling farmers enhance crop yield while optimizing resource utilization. confirms our hypothesis, showcasing effectiveness combining ViT addressing complex agricultural challenges.

Language: Английский

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A smartphone-based application for an early skin disease prognosis: Towards a lean healthcare system via computer-based vision

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102036 - 102036

Published: May 30, 2023

Language: Английский

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Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(12), P. 2988 - 2988

Published: June 8, 2023

The categorization and identification of agricultural imagery constitute the fundamental requisites contemporary farming practices. Among various methods employed for image classification recognition, convolutional neural network (CNN) stands out as most extensively utilized swiftly advancing machine learning technique. Its immense potential precision agriculture cannot be understated. By comprehensively reviewing progress made in CNN applications throughout entire crop growth cycle, this study aims to provide an updated account these endeavors spanning years 2020 2023. During seed stage, networks are effectively categorize screen seeds. In vegetative recognition play a prominent role, with diverse range models being applied, each its own specific focus. reproductive CNN’s application primarily centers around target detection mechanized harvesting purposes. As post-harvest assumes pivotal role screening grading harvested products. Ultimately, through comprehensive analysis prevailing research landscape, presents characteristics trends current investigations, while outlining future developmental trajectory classification.

Language: Английский

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Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 72509 - 72517

Published: Jan. 1, 2023

Current technological advancement in computer systems has transformed the lives of humans from real to virtual environments. Malware is unnecessary software that often utilized launch cyber-attacks. variants are still evolving by using advanced packing and obfuscation methods. These approaches make malware classification detection more challenging. New techniques different conventional should be for effectively combating new variants. Machine learning (ML) methods ineffective identifying all complex The deep (DL) method can a promising solution detect This paper presents an Automated Android Detection Optimal Ensemble Learning Approach Cybersecurity (AAMD-OELAC) technique. major aim AAMD-OELAC technique lies automated identification malware. To achieve this, performs data preprocessing at preliminary stage. For process, follows ensemble process three ML models, namely Least Square Support Vector (LS-SVM), kernel extreme machine (KELM), Regularized random vector functional link neural network (RRVFLN). Finally, hunter-prey optimization (HPO) approach exploited optimal parameter tuning DL it helps accomplish improved results. denote supremacy method, comprehensive experimental analysis conducted. simulation results portrayed over other existing approaches.

Language: Английский

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Deploying deep convolutional neural network to the battle against cancer: Towards flexible healthcare systems

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 47, P. 101494 - 101494

Published: Jan. 1, 2024

The complexity of the facilities healthcare providers goes beyond their physical articulation, function, and organization; it also involves integrating technology activities that continuously evolve due to medical research technological advancements. As a result, hospitals require flexible approach can accommodate changing demands patients, professionals, researchers. This flexibility is essential in ensuring meet diverse needs users adapt fast-changing requirements. Therefore, analytical capabilities Machine Learning algorithms services vital aspect Flexible Healthcare Systems. Furthermore, enables efficiently organize patient data optimize treatment plans by analyzing vast amounts data. In this paper, we explored role applying Deep Convolutional Neural Networks on three unique datasets predict risk developing cancer using health informatics demonstrate how computer-based vision improve prognosis images. have employed advanced CNNs for high-accuracy detection images, streamlined model combines feature-detecting convolutional layers with complexity-reducing pooling which ensures effective identification. implementation these models into delivery potentially outcomes system-level efficiencies, but carefully considering limitations ethical implications are essential.

Language: Английский

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Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 133(11-12), P. 5343 - 5419

Published: July 2, 2024

Language: Английский

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