Lung Segmentation Using Proposed Deep Learning Architecture

Ayad, Hayder; Ghindawi, Ikhlas Watan; Kadhm, Mustafa Salam

doi:10.3991/ijoe.v16i15.17115

Cited by 7 publications

(10 citation statements)

References 8 publications

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“…Saif et al (2020) used VGG16 as CNN model to diagnose 1500 of lung cancer images, the result achieve 98.55% of accuracy. Ayad et al (2020) proposed a CT-based lung segmentation method that used the weighted Softmax function. Using the LIDC-IDRI CT lung images database, the system obtained a maximum segmentation accuracy of 98.90%.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and CLAHE framework

Hadiyoso

Aulia²,

Irawati³

2023

Int. J. Appl. Sci. Eng.

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Cancer is a non-contagious disease that is the leading cause of death globally. The most common types of cancer with high mortality are lung and colon cancer. One of the efforts to reduce cases of death is early diagnosis followed by medical therapy. Tissue sampling and clinical pathological examination are the gold standard in cancer diagnosis. However, in some cases, pathological examination of tissue to the cell level requires high accuracy, depending on the contrast of the pathological image, and the experience of the clinician. Therefore, we need an image processing approach combined with artificial intelligence for automatic classification. In this study, a method is proposed for automatic classification of lung and colon cancer based on a deep learning approach. The object of the image that is classified is the histopathological image of normal tissue, benign cancer, and malignant cancer. Convolutional neural network (CNN) with VGG16 architecture and Contrast Limited Adaptive Histogram Equalization (CLAHE) were employed for demonstration of classification on 25000 histopathological images. The simulation results show that the proposed method is able to classify with a maximum accuracy of 98.96%. The system performance using CLAHE shows a higher detection accuracy than without using CLAHE and is consistent for all epoch scenarios. The comparative study shows that the proposed method outperforms some previous studies. With this proposed method, it is hoped that it can help clinicians in diagnosing cancer automatically, with low cost, high accuracy, and fast processing on large datasets.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and CLAHE framework

Hadiyoso

Aulia²,

Irawati³

2023

Int. J. Appl. Sci. Eng.

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“…A few unique sorts of layers are frequently utilized in CNNs [20,21], such as loss, convolutional, ReLu (Rectified Linear Units), pooling, and fully connected layer [22]. Deep neural network used in many fields such as the study [23][24][25][26][27][28].…”

Section: Figure 3 Overview Of a Convolution Neural Network [16]mentioning

confidence: 99%

Facial Emotion Recognition Using HOG and Convolution Neural Network

Ghalib¹,

Ibrahim²,

Noor³

2023

ISI

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Due to numerous difficulties, including the variation in face shapes between individuals, the challenge of recognizing dynamic facial attributes, the poor quality of digital images, etc., detecting human emotion depending on facial expression is difficult for the computer vision community. Thus, in this study, we propose an approach for emotion recognition depending on facial expression using histogram of oriented gradients and convolution neural network (HOG-CNN). The HOG-CNN composed of three stages, median filter, HOG, and CNN. The first stage is preprocessing using median filter. The second stage is feature extraction using HOG. The third stage is classification using CNN. The proposed method was tested and evaluated on the UMD face database. The system attained a high performance with a mean average accuracy of 98.07%, average precision of 94.78%, and average recall of 97.15%.

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“…Medical imaging analysis plays a critical role in diagnosing disease such as diabetic retinopathy [19], skin cancer [20] and lung disease [21]. In literature, several studies have been reported for automatic segmentation of myocardial scar and edema.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

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Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively.

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Lung Segmentation Using Proposed Deep Learning Architecture

Cited by 7 publications

References 8 publications

Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and CLAHE framework

Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and CLAHE framework

Facial Emotion Recognition Using HOG and Convolution Neural Network

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

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