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End-to-End Lung Cancer Diagnosis on Computed Tomography Scans using 3D CNN and Explainable AI

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Chaitanya Rahalkar, Anushka Virgaonkar, Dhaval Gujar, Sumedh Patkar
10.5120/ijca2020920111

Chaitanya Rahalkar, Anushka Virgaonkar, Dhaval Gujar and Sumedh Patkar. End-to-End Lung Cancer Diagnosis on Computed Tomography Scans using 3D CNN and Explainable AI. International Journal of Computer Applications 176(15):1-6, April 2020. BibTeX

@article{10.5120/ijca2020920111,
	author = {Chaitanya Rahalkar and Anushka Virgaonkar and Dhaval Gujar and Sumedh Patkar},
	title = {End-to-End Lung Cancer Diagnosis on Computed Tomography Scans using 3D CNN and Explainable AI},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2020},
	volume = {176},
	number = {15},
	month = {Apr},
	year = {2020},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume176/number15/31274-2020920111},
	doi = {10.5120/ijca2020920111},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Lung cancer is one of the most common malignant neoplasms all over the world. It accounts for more cancer deaths than any other cancer. It is increasingly being recognized in hospitals all across the globe. With the increasing prevalence of smoking, Lung cancer has reached epidemic proportions. Thus, we propose a 3D-CNN-based model [6] that uses a patient’s Computed Tomography scans to detect nodules and check for malignancy. We intend to add an explainable aspect to the result since the central problem of such models is that they are regarded as black-box models, and they lack an explicit declarative knowledge representation [9]. This calls for systems enabling to make decisions transparent, understandable and explainable.

References

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Keywords

Supervised Learning, Convolutional Neural Networks