Lung cancer detection and classification on CT scan images  using enhanced artificial bee colony optimization

Perumal, S. Arumuga; Velmurugan, T.

doi:10.14419/ijet.v7i2.26.12538

Cited by 25 publications

(11 citation statements)

References 23 publications

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“…Fuzzy algorithm used to detect the region that was abnormal and to segment the lesion area in lung. [7] Improvements of image were made by using contrast steady adaptable histogram equalization. Furthermore, segmentation with watershed marker procedure have been used.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Akkar¹,

Haddad²

2020

ETJ

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Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work () computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and () Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis for lung.

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Section: Related Workmentioning

confidence: 99%

Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Akkar¹,

Haddad²

2020

ETJ

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“…S.Perumal et al [20] in 2018 documented an enhanced ABC optimization for cancer detection and classification. 92 percent proficiency is accomplished by using this procedure.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection using Bio-Inspired Algorithm in CT Scans and Secure Data Transmission through IoT Cloud

Venkatesh¹,

Bojja²

2020

IJACSA

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Primary recognition of pulmonary cancer nodules eloquently increases the odds of survival, also leads it solider problem to resolve, as it often relies on a tomography scan filmic examination. By increasing the possibility of effective treatment, earlier tumor diagnosis decreases lung cancer mortality. Radiologists usually diagnose lung cancer on medical images by a systematic analysis that consumes more time and is unreliable often, because of the substantial improvement in the transmission of data in the healthcare sector, the protection and integrity of medical data has been a huge problem for healthcare applications. This study utilizes computational intelligence techniques. For detection and data transmission, a novel Hybrid model is therefore proposed in this paper. Two steps are involved in the proposed method where diverse image processing procedures are used to detect cancer in the first step using MATLAB and data transfer to authorized persons via the IoT cloud in the second stage. The simulated steps include preprocessing, segmentation by Otsu thresholding along with swarm intelligence algorithm, extraction of features by local binary pattern and classification using the support vector machine (SVM). This work demonstrates the dominance of swarmintelligent framework over the conventional algorithms in terms of performance metrics like sensitivity, accuracy and specificity as well as training time. The tests carried out show that the model built can achieve up to 92.96 percent sensitivity, 93.53 percent accuracy and 98.52 percent specificity.

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“…S.Perumalet.al [10 ]. in 2018, a cancer detection and classification scheme was suggested by Enhanced ABC optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Qualitative Model for Detection of Lung Cancer using Optimization

Venkatesh¹,

Bojja²

2019

IJITEE

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As of late, expectation of cancer at prior stages is mandatory to increase the opportunity of survival of the harassed. The most appalling sort is lung cancer, which is most common malady these days. So to dispose of it a detection framework is proposed. The objective of this paper is to investigate a practical segmentation algorithm with optimization system for therapeutic images to abridge the doctors' understanding of CT images. Recent medicinal imaging modalities produce enormous images that are incredibly terrible to examine physically. The outcomes of segmentation algorithm depend on the exactitude and intermingling time. In this paper, a qualitative detection model is proposed to partition the CT images of lung cancer. The detection framework shaped the obtained therapeutic images of lung CT images. To begin with, in pre-processing stage the median filter is utilized for noise reduction and smoothing. Later Otsu’s segmentation is applied to separate locale of enthusiasm from lung cancer images along with particle swarm optimization to get more accuracy and also for feature extraction LBP is connected. Here, the proposed model is framed by utilizing SVM technique for classification. Using MATLAB, simulation results are obtained for cancer detection system and these results are compared with other optimization techniques.

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Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization

Cited by 25 publications

References 23 publications

Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Lung Cancer Detection using Bio-Inspired Algorithm in CT Scans and Secure Data Transmission through IoT Cloud

Development of Qualitative Model for Detection of Lung Cancer using Optimization

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