Automated Melanoma Staging in Lymph Node Biopsy Image using Deep Learning

Alheejawi, Salah; Mandal, Mrinal; Berendt, Richard; Jha, Naresh

doi:10.1109/ccece.2019.8861878

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…Alheejawi et al [11] suggested a DL based methodology for automatically measuring the PI values in Ki-67-stained biopsy image. The transmission of melanoma cancer in the human body could be determined through the PI in lymph node biopsy.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Melanoma Cancer Detection Techniques

Kaur

2021

SN COMPUT. SCI.

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Cancerous melanoma can be easily distinguished from non-cancerous melanoma by considering few exclusive attributes. These features are selected to be utilized in the final phase of classification process. The pigmented lesion is classified by extracting the attributes in accordance with the obtained ABCD. These attributes can be analyzed for quantifying a mole as a common injury or melanoma. The cancer can be detected using different phases in which pre-processing is performed, features are extracted and the classification is carried out. This paper reviews several methods to detect melanoma cancer with regard to certain performance metrics.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Melanoma Cancer Detection Techniques

Kaur

2021

SN COMPUT. SCI.

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“…However, it is a tedious task to gather images of all types of cancers with appropriate labels. Salah Alheejawi et al [ 8 ] developed a technique for the regular measurement of PI values in i-67 stained biopsy images using a deep-learning algorithm. This model segments the nuclei and evaluates the PI by using a trained CNN model.…”

Section: Literature Surveymentioning

confidence: 99%

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Balaji

Chidambaram

2021

Diagnostics

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One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

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“…In recent years, the rapid development of artificial intelligence technology has provided new methods for image segmentation. Image segmentation methods based on deep learning have also received a lot of attention [17]. In 2015, Long et al [18] built a fully convolutional network (FCN) for image semantic segmentation, which completed the pixel-level image segmentation task.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust

Yang¹,

Xu²,

Wei³

et al. 2022

Computers, Materials &Amp; Continua

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Zanthoxylum bungeanum Maxim, generally called prickly ash, is widely grown in China. Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum. Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience. Due to the complex colors and shapes of rust areas, the accuracy of manual recognition is low and difficult to be quantified. In recent years, the application of artificial intelligence technology in the agricultural field has gradually increased. In this paper, based on the DeepLabV2 model, we proposed a Zanthoxylum rust image segmentation model based on the FASPP module and enhanced features of rust areas. This paper constructed a fine-grained Zanthoxylum rust image dataset. In this dataset, the Zanthoxylum rust image was segmented and labeled according to leaves, spore piles, and brown lesions. The experimental results showed that the Zanthoxylum rust image segmentation method proposed in this paper was effective. The segmentation accuracy rates of leaves, spore piles and brown lesions reached 99.66%, 85.16% and 82.47% respectively. MPA reached 91.80%, and MIoU reached 84.99%. At the same time, the proposed image segmentation model also had good efficiency, which can process 22 images per minute. This article provides an intelligent method for efficiently and accurately recognizing the degree of infection of Zanthoxylum rust.

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Automated Melanoma Staging in Lymph Node Biopsy Image using Deep Learning

Cited by 11 publications

References 9 publications

Analysis of Melanoma Cancer Detection Techniques

Analysis of Melanoma Cancer Detection Techniques

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust

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