Convolutional Neural Network and Support Vector Machine in Classification of Flower Images

Peryanto, Ari; Yudhana, Anton; Umar, Rusydi

doi:10.23917/khif.v8i1.15531

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…In Table 3, we can conclude that Opti-SA on the 4C flower dataset has the satisfactory performance, according to the result of accuracy, percision, recall, specificity and F1-score [49,52]. F1-score performs about 2% higher on 4C flower dataset than 3C flower dataset.…”

Section: ) Discussionmentioning

confidence: 89%

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

Fei

Zhu

et al. 2023

IEEE Access

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In the process of classifying fresh-cut flowers, the classification accuracy of the algorithm plays a vital role in the control of quality stability, uniformity, and price of fresh-cut flowers, while the classification speed of an algorithm determines the possibility of industrial application. Currently, research on fresh-cut flower classification focuses on the breakthrough of classification accuracy, ignoring the real-time processing speed of the terminal, which seriously affects the use of fresh-cut flower online classification technology. In this study, RGB images and depth information data for 434 rose flowers were collected using a binocular stereo depth camera. Combined with the actual production line classification environment, a set of data argumentation solutions was developed under the condition of limited samples. The architecture was established and optimized based on the ShuffleNet V2 network backbone unit, transfer learning was performed, and an appropriate attention mechanism was invoked to classify flowers of five specifications. The experimental results showed that the proposed network structure had a competitive advantage in terms of parameter quantity, classification speed, and accuracy compared with traditional networks without an attention mechanism and other lightweight networks. The classification accuracy on the 3-channel (RGB channel) flower dataset and the 4-channel (RGB and depth channel) flower datasets were 98.891% and 99.915%, respectively, and the overall prediction classification speed can reach 0.020 seconds per flower. Compared to the fresh-cut flower classification machines currently on the market, the speed of the proposed method has a great advantage. These advantages are of great significance for the design and development of fresh-cut flower classification and grading systems, and the proposed method is instructive for the identification and application of multichannel data in the future.

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Section: ) Discussionmentioning

confidence: 89%

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

Fei

Zhu

et al. 2023

IEEE Access

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“…It is commonly used for regression and classification tasks but is particularly effective in classification problems 35 . Mawarni et al have shown the practical application of the SVM algorithm on external wound classification 36 . Wang et al also revealed that SVM could be effective in the classification of diabetic ulcers 37 .…”

Section: Discussionmentioning

confidence: 99%

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

Zhang,

Zhao,

Farzan

et al. 2024

International Wound Journal

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Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like ‘machine learning’, ‘surgical’ and ‘wound’. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn‐grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.

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“…CNN processing sequential model in python, while Faster R-CNN utilizes the framework from Tensorflow as framework of an open-source deep learning framework with a library to perform computational calculations with high performance [17]. The model is based on CNN [23], which acts as a backbone and produces an algorithm that can detect objects to perform classification quickly [24].…”

Section: Methodsmentioning

confidence: 99%

Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison

Sunardi

Yudhana

WindraPutri

2022

KINETIK

Self Cite

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Threat of breast cancer is a frightening type and threatens the female population worldwide. Early detection is preventive solution to determine cancer diagnosis or tumors in the female breast area. Today, machine learning technology in managing medical images has become an innovative trend in the health sector. This technology can accelerate diagnosing disease based on the acquisition of accuracy values. The primary purpose of this research is to innovate by comparing two deep learning models to build a prediction system for early-stage breast cancer. This research utilizes Convolutional Neural Network (CNN) sequential models and Faster Region-based Convolutional Neural Network (R-CNN) models that can determine the classification of normal or abnormal breast image data, which can determine the normal or abnormal classification of breast image. The dataset's source in this study came from the Mammographic Image Analysis Society (MIAS). This dataset consists of 322 mammogram data with 123 abnormal and 199 normal classes. The experimental results of this study show that the accuracy of the CNN and R-CNN models in image classification are 91.26% and 63.89%, respectively. Based on these results, the CNN sequential model has better accuracy than the Faster R-CNN model, because it does not require unique characteristics to detect breast cancer.

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Convolutional Neural Network and Support Vector Machine in Classification of Flower Images

Cited by 15 publications

References 12 publications

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison

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