Colorectal Polyp Detection Using Feedforward Neural Network with Image Feature Selection

Muhammad, Arif Wirawan; Sasmito, Ginanjar Wiro; Riadi, Imam

doi:10.1109/sain.2018.8673371

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…Energy reflects the degree of texture smoothness of an image. The lower the energy value, the rougher the surface texture of the image and vice versa [12]. The calculation of the energy value is presented in (4)…”

Section: Energymentioning

confidence: 99%

“…Contrast is a unit of local image variation values [12] [13]. The calculation of the contrast value is presented in (5)…”

Section: Energymentioning

confidence: 99%

“…At the input layer of the artificial neural network architecture, 4 neurons and 5 neurons are used following the number of feature extractions from the colonic endoscopy image. While the hidden layer / intermediate layer uses 9 and 11 neurons following the equation stated by [12][15] that the use of a hidden layer of 2n+1 (where n is the number of input neurons) can accelerate the training process and the generalization results of neural networks. At the output layer, the binary-shot coding concept is used where 1 represents the condition of the cancerous image, while 0 represents the normal image condition [16].…”

Section: Homogeneitymentioning

confidence: 99%

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Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Athiyah¹,

Muhammad

Azhari

2020

KEDS

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I. Introduction Colorectal cancer is a type of cancer that attacks the intestinal walls cell of humans. Wisconsin Reporting System (WRS), states that the type of colorectal cancer is the third highest cause of death after types of lung cancer and breast cancer, with a case of death of 9.5% of the total world population [1]. In Indonesia, colorectal cancer itself is ranked as the third cause of death after breast cancer and cervical cancer. Therefore, the abnormalities in the human intestinal wall need to be identified early to minimize the growth of cancerous cells that are more virulent, which can cause death. Health screening, such as endoscopy, is a simple step to detect abnormal growths in human intestinal cells wall [2][3]. Also, the process of early detection plays an essential role in health practitioners/gynecologists to determine the prognosis and type of treatment that patients must receive [4]. The right prognosis is accompanied by the right dosage of the drug to help speed the recovery of patients from colorectal cancer outbreaks. Endoscopic screening technique is a common step carried out by the health experts/gynecologist to determine the condition of the human intestine by inserting a camera through the rectum to get an intestine picture [5]. The result of endoscopic screening is a digital image of the area around the intestinal wall [6]. During this time, the interpretation of endoscopic images is carried out manually or taken by the naked eye, thus requires quite a long time to interpret and produce results. This ARTICLE INFO

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Section: Energymentioning

confidence: 99%

“…Contrast is a unit of local image variation values [12] [13]. The calculation of the contrast value is presented in (5)…”

Section: Energymentioning

confidence: 99%

Section: Homogeneitymentioning

confidence: 99%

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Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Athiyah¹,

Muhammad

Azhari

2020

KEDS

Self Cite

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“…The development of artificial intelligence (AI), especially machine learning, makes everything easier to apply. In accordance with its advantages, one part of ML is a supervised learning algorithm, which can be used not only for control and monitoring systems [2], but also in data science where it can be implemented in a data classification that applies in various fields, such as health [3], livestock and agriculture [4], [5], economy and industry [6], transportation [7], education, health [8] and others [9].…”

Section: Introductionmentioning

confidence: 99%

SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

et al. 2021

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An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.

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Colorectal Polyp Detection Using Feedforward Neural Network with Image Feature Selection

Cited by 2 publications

References 19 publications

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

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