Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network

Hajabdollahi, Mohsen; Esfandiarpoor, Reza; Sabeti, Elyas; Karimi, Nader; Soroushmehr, S. M. Reza; Samavi, Shadrokh

doi:10.1016/j.bspc.2019.101792

Cited by 24 publications

(12 citation statements)

References 46 publications

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“…Other than feature extraction, CNN has been studied for detection, classi cation and segmentation tasks in medical research. For example, [8] proposed a CNN-based double-branched model wherein one branch was utilized for feature extraction, the other for segmentation for multiple abnormality detection from medical images. [9] proposed the CemrgApp, a CNN model, to classify cardiovascular properties from cardiovascular magnetic imaging (CMRI) scans of different cardiac patients, for e cient diagnosis and treatment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed framework was trained on 207 manually annotated CMRI scans, ultimately achieving a Dice score of 0.91 ± 0.02 for atrial blood pool segmentation. [10] and [11] implemented CNNs and their variants in automatic lesion detection, and multiple abnormality detection from medical images. [12] developed a deconvolutional CNN for classi cation of acute lymphoblastic leukemia, a type of cancer of the white blood cell.…”

Section: Literature Reviewmentioning

confidence: 99%

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Identifying Malignant Lymph Nodes of Prostate Cancer Patients Using A Combination of Pre-Trained Deep Models and Traditional Machine Learning Classifiers

Sarkar

Harwood

et al. 2021

Preprint

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Prostate cancer is the second most common new cancer diagnosis in the United States. The prostate gland sits beneath the urinary bladder and surrounds the first part of the urethra. Usually, prostate cancer is slow-growing; stays confined to the prostate gland; and can be treated conservatively (active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that suggests the cancer is more aggressive and surgery is not adequate. In those cases, radiation and/or systemic therapies (e.g., chemotherapy, immunotherapy) are required. The challenge is that it is often difficult for radiologists to differentiate malignant lymph nodes from non-malignant ones with current medical imaging technology. In this study, we design a scalable hybrid approach utilizing a deep learning model to extract features into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Identifying Malignant Lymph Nodes of Prostate Cancer Patients Using A Combination of Pre-Trained Deep Models and Traditional Machine Learning Classifiers

Sarkar

Harwood

et al. 2021

Preprint

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“…Hidden Markov models were proposed without precedent in the late 1960's mid 1970's [20]. What's more, are stochastic models for successive data [21].…”

Section: Parametric Approachesmentioning

confidence: 99%

Survey on Novelty Detection using Machine Learning Techniques

Ouafae¹,

Oumaima²,

Mariam³

et al. 2021

Adv. sci. technol. eng. syst. j.

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Novelty detection affords to identify data patterns that stray strikingly from the normal behavior. it allows a good identification and classification of objects which were not known during the learning phase of the model. In this article, we will introduce an organized and comprehensive review of the study on novelty detection. We have grouped existing methods into three classes. Statistical Based techniques, Machine Learning Based techniques and Deep Learning Based techniques. In addition, we provide a discussion on application domains of novelty detection, and for each category, we have defined the novelty, cited the most used dataset, as well as a description and perspectives of the latest work carried out in this domain. Our article is developed with the aim of facilitating to researchers a better understanding of the interest of using novelty detection in the various fields mentioned in the article, as well as to clarify the different existing novelty detection methods.

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“…Proposed framework is trained on three datasets, ISIC 2016, ISIC 2017, and ISIC 2018, to achieving the promising results. Similarly, a pool of researchers [39][40][41] are utilizing deep frameworks to detect multiple abnormalities with an application to skin lesion classification.…”

Section: Literature Reviewmentioning

confidence: 99%

A multilevel features selection framework for skin lesion classification

Akram

Lodhi

Naqvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has increased tremendously over the past three decades [1]. According to statistics provided by the World Health Organization (WHO), almost 132,000 new cases of melanoma are reported each year worldwide. It has been reported [2] that diagnosis of melanoma, in its early stages, significantly increases chances of the patient's survival. Dermatoscopy, also knows as dermoscopy is a non-invasive clinical procedure used for

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Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network

Cited by 24 publications

References 46 publications

Identifying Malignant Lymph Nodes of Prostate Cancer Patients Using A Combination of Pre-Trained Deep Models and Traditional Machine Learning Classifiers

Identifying Malignant Lymph Nodes of Prostate Cancer Patients Using A Combination of Pre-Trained Deep Models and Traditional Machine Learning Classifiers

Survey on Novelty Detection using Machine Learning Techniques

A multilevel features selection framework for skin lesion classification

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