Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M. Iqbal; Ramli, Abdul Rahman

doi:10.1007/s10278-017-9958-5

Cited by 29 publications

(18 citation statements)

References 42 publications

(61 reference statements)

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“…The position of the SSA population, with n salps, is initiated randomly according to Algorithm (1). The fitness function is then calculated in step (5) for each salp to get the best salp position and assign it to the F best variable as the source food in step (6). The a 1 parameter is calculated by Eq.…”

Section: B Salp Swarm Algorithm (Ssa)mentioning

confidence: 99%

“…The CT imaging is not used in a wide range although it features more enhanced images of breast tissue than other methods. That is because the detection of unhealthy cases of breast cancer with accurate results needs to merge CT with another screening technique besides the dose of radiation it might inflect on the screened subjects [5], [6]. The thermal screening method is a fast, simple, safe, low cost, and can detect tumor growth in an early stage.…”

Section: Introductionmentioning

confidence: 99%

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Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

et al. 2020

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Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in singleobjective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to realworld thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.

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Section: B Salp Swarm Algorithm (Ssa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

et al. 2020

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“…With the development of modern medical techniques, researchers have proved that the survival rate could achieve 54% on average if the lung cancers could be diagnosed in the early stage [3]. Therefore, early detection of pulmonary nodule plays a critical role in the early diagnosis of lung cancers [4] and computer-assisted diagnosis system (CADs) [5]. Recently, pulmonary nodule detection has been typically performed on the chest CT scans, and many automated detection methods have been proposed by processing and analyzing the chest CT images [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Chi

Zhang

et al. 2020

Sensors

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Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the “coarse-to-fine” detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity.

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“…Technological advances capable of adopting human processes and ways of thinking are Artificial Intelligence technology (Graf & Schindler, 2014) or artificial intelligence. One of artificial intelligence technology is an expert system (Chuanlei, Shanwen, Jucheng, Yancui, & Jia, 2017) Which is a computer program that can mimic the process of thinking and expert knowledge to solve a problem that specifications (Jalalian et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The method of production rules is written in if-then (if-then). The IF-then rule connects the antecedent to the consequences of it (Jalalian et al, 2017;Mahmoodi, Mirzaie, & Mahmoudi, 2016). This rule consists of two parts of a premise (IF) containing things or information that is deemed correct and part of the conclusion (THEN) which contains the conclusions or results of the interconnected premise (Kusrini & Luthfi, 2009).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Plant Fragaria Xananassa Disease Diagnoses Using Production Rules Base on Expert System

Basiroh¹,

Lestari

2020

pilar

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Errors that occur in solving problems in strawberry plants (Fragaria Xananassa) such as the presence of leaf patches, fruit rot, perforated leaves, and insect pests can be the cause of not maximum in harvest time. The farmers and the general public who planted strawberry (Fragaria Xananassa) need to know the proper treatment of diseases and pests so that future yields as expected. Therefore, it takes an application as a solution in the delivery of information related to the problems that are often encountered in strawberry plants (Fragaria Xananassa). Methods of production rules can be used to diagnose the disease strawberry (Fragaria Xananassa) based on signs or symptoms that occur in the parts of plants and strawberry, the results of diagnosis using this method are the same as we do Consultation on experts. The purpose of this study was to determine the early diagnosis of disease in strawberry plants (Fragaria Xananassa) based on signs or symptoms that occur in the plant and fruit parts. The results of the analysis of this study showed that the validation of disease and symptom data in strawberry plants (Fragaria Xananassa) reached 99%, meaning that between the data of symptoms and disease understudy the accuracy was guaranteed with the experts.

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Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

Cited by 29 publications

References 42 publications

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Analysis of Plant Fragaria Xananassa Disease Diagnoses Using Production Rules Base on Expert System

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