[Retracted] Dense Convolutional Neural Network for Detection of Cancer from CT Images
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Post-publication record
- Nature
- Retraction
- Reason
- Compromised Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Unreliable Results and/or Conclusions;
- Date
- 12/29/2023 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.
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The record
- Venue
- BioMed Research International
- Topic
- Radiomics and Machine Learning in Medical Imaging
- Field
- Medicine
- Canadian institutions
- Toronto Metropolitan University
- Funders
- Saint Joseph UniversityKing Saud UniversitySaveetha Dental College
- Keywords
- Convolutional neural networkCancerComputer scienceArtificial intelligencePattern recognition (psychology)MedicineRadiologyInternal medicine
- Has abstract in OpenAlex
- yes