Medical Image Segmentation Using a Robust Edge-stop Function with 2×2 Window Patch

Pratondo, Agus; Roedavan, Rickman; Sujana, Aprianti Putri; Rizqyawan, Muhammad Ilham

doi:10.1109/icset51301.2020.9265376

Cited by 19 publications

(3 citation statements)

References 15 publications

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“…Deep learning, especially transfer learning, can be used to easily classify different variants of cassava leaves. With the help of a pre-trained neural network and modern computer support, it is possible to accurately identify each variant based on its unique characteristics and features [8][9][10][11][12][13]. The transfer learning approach allows for the neural network to be adapted to the specific task of identifying cassava leaves, even with limited , on a dataset of labeled cassava leaf images, the model can learn to differentiate between the different variants.…”

Section: Introductionmentioning

confidence: 99%

Classification of cassava leaves (Manihot esculenta) using transfer learning

Pratondo

2023

IJEEI

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There are several types of cassava leaves with different characteristics, tastes, and nutritional content. Some people use cassava leaves as a vegetable ingredient for daily consumption as a source of fiber and minerals. However, people often have difficulty identifying the different types of cassava leaves, including cassava leaf variants that are locally referred to as gajah, karet, and mentega. This study aims to use transfer learning to identify the variant of cassava leaves. The Inception v3 architecture was selected to build the classification model. To demonstrate the superiority of transfer learning, the Inception v3 architecture was run with two different weights. The first weight was randomly initialized, while the second weight was taken from pre-trained weights from ImageNet. The experimental results show that the classification accuracy rate using the pre-trained weights reached 95.76%. This indicates that the classification model used in this study is promising and can be used for practical purposes in everyday life.

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Section: Introductionmentioning

confidence: 99%

Classification of cassava leaves (Manihot esculenta) using transfer learning

Pratondo

2023

IJEEI

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“…Computer vision has played an important role in various fields, including biomedical engineering ( Rizqyawan et al, 2020 ; Pratondo et al, 2020 ; Zunair & Hamza, 2020 ; Rizqyawan et al, 2022 ), smart farming, and agriculture ( Rajasekaran et al, 2020 ; Jahanbakhshi et al, 2021 ; Pratondo & Bramantoro, 2022 ; Pratondo & Harahap, 2022 ). The use of smart technology in those fields have been widely used to support many activities, e.g ., seed classification, disease detection, automatic watering, and quality assessment ( Pratondo, Ong & Chui, 2014 ; Budiwati et al, 2021 ; Jong & Pratondo, 2011 ; Pratondo, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

“…The intervention of artificial intelligence, such as deep learning techniques, is innovative for this purpose. Applying computational technology to various fields leads to a better life ( Mutiara, Hapsari & Pratondo, 2019 ; Pratondo et al, 2020 ; Rizqyawan et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Classification ofCurcuma longaandCurcuma zanthorrhizausing transfer learning

Pratondo

Elfahmi

Novianty

2022

PeerJ Computer Science

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Curcuma longa (turmeric) and Curcuma zanthorrhiza (temulawak) are members of the Zingiberaceae family that contain curcuminoids, essential oils, starch, protein, fat, cellulose, and minerals. The nutritional content proportion of turmeric is different from temulawak which implies differences in economic value. However, only a few people who understand herbal plants, can identify the difference between them. This study aims to build a model that can distinguish between the two species of Zingiberaceae based on the image captured from a mobile phone camera. A collection of images consisting of both types of rhizomes are used to build a model through a learning process using transfer learning, specifically pre-trained VGG-19 and Inception V3 with ImageNet weight. Experimental results show that the accuracy rates of the models to classify the rhizomes are 92.43% and 94.29%, consecutively. These achievements are quite promising to be used in various practical use.

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Classification of Longan Edibility using Machine Learning

Pratondo

Novianty

2022

2022 1st International Conference on Smart Technology, Applied Informatics, and Engineering (APICS)

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Medical Image Segmentation Using a Robust Edge-stop Function with 2×2 Window Patch

Cited by 19 publications

References 15 publications

Classification of cassava leaves (Manihot esculenta) using transfer learning

Classification of cassava leaves (Manihot esculenta) using transfer learning

Classification ofCurcuma longaandCurcuma zanthorrhizausing transfer learning

Classification of Longan Edibility using Machine Learning

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