Computer-Aided Detection for Early Detection of Lung Cancer Using CT Images

Desai, Usha; Kamath, S. Sowmya; Shetty, Akshaya D.; Prabhu, M.

doi:10.1007/978-981-16-2422-3_24

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…When entering the main program, by clicking the AR scan button, the author directs the camera to the marker that has been printed out and places the marker somewhere. The marker will perform the detection and will display a 3D object (Desai et al, 2022;Kwakye, 2010). In the main program, there is also a Description button which functions to display a description of the detected leaf image.…”

Section: Marker Detection Output Displaymentioning

confidence: 99%

Revolutionizing Biology Learning Through Ar: The Case of Leafcapture Application Development

Lismaya,

Nurlaelah,

Handayani

2024

IJLI

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This study aims to produce learning media in the form of a Leafcapture Identification Application Based on Augmented Reality (AR) in plant morphology courses with accuracy, user interface, and good content. The research method used in this study is Research and Development (RD) with the The ADDIE approach involves a systematic process for designing learning media. In the Analyze stage, the focus is on identifying the need for new learning media, specifically the Leafcapture Identification Application Based on AR, and assessing its feasibility and requirements. In the Design stage, activities include application design, interface design, 3D image creation, and marker design. The Development stage involves gathering relevant content, such as plant leaf images and names, and setting up the application interface. During Implementation, the developed media is used and evaluated by experts and users to assess its impact on learning quality. Media experts validated the product with a 75% approval rating, while subject matter experts found it 88.89% valid. Evaluation was conducted by surveying students in plant morphology courses, with results confirming the product's effectiveness. Through AR, students can directly engage with three-dimensional (3D) models of various parts of plants, such as roots, stems, leaves, flowers, and fruits. They can manipulate and examine each part more deeply, enhancing their understanding of the relationships between parts and the functions of each component. Additionally, AR makes learning plant morphology more engaging and lifelike.Keywords: Augmented Reality; biology; Leafcapture identification.

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Section: Marker Detection Output Displaymentioning

confidence: 99%

Revolutionizing Biology Learning Through Ar: The Case of Leafcapture Application Development

Lismaya,

Nurlaelah,

Handayani

2024

IJLI

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“…Desai et al [ 66 ] propose an automatic Computer-Aided Detection for early diagnosis, and classification of lung cancerous abnormalities, where the segmentation process is performed through Marker-controlled watershed segmentation and the K-Means algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The K-Means algorithm is implemented as a means to simplify image data and propose image regions based on color differences, while the Watershed algorithm performs analysis on the resulting infographical map for extracting the proposed regions, with the help of 8-way component analysis for component separation and contour detection for the final extraction. Although there are some proposals for image segmentation based on the K-Means and Watershed algorithms [ 65 , 66 , 67 , 68 ], to the best of our knowledge, the approach architecture we present in this paper was not previously proposed. The developed method was applied in the segmentation of trademark graphic images, which we consider to be a challenge given their characteristics.…”

Section: Introductionmentioning

confidence: 99%

Graphical Image Region Extraction with K-Means Clustering and Watershed

Jardim

António²,

Mora

2022

J. Imaging

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With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

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“…The CT scan technique is most widely used in clinics for nodule identification and for its diagnostics. Unlike other techniques, the CT scan is very effective as it provides detail images in three dimensions of any tissue in human body and avoids the overlapping of layers of various tissues in images [ 9 ]. According to National Lung Screening Trial by Cancer Institute, the use of CT scan in detecting the nodule reduces the mortality of cancer by 20% in lungs in recent years.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

JAVED

Liaqat

Meraj

et al. 2023

Computational Intelligence and Neuroscience

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Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient’s survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.

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Computer-Aided Detection for Early Detection of Lung Cancer Using CT Images

Cited by 5 publications

References 20 publications

Revolutionizing Biology Learning Through Ar: The Case of Leafcapture Application Development

Revolutionizing Biology Learning Through Ar: The Case of Leafcapture Application Development

Graphical Image Region Extraction with K-Means Clustering and Watershed

Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

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