Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images Subject The topic of the resource Cancer Detection; CNN; computer aided diagnosis; Deep Learning; Histopathological Images; image Classification; machine learning Description An account of the resource Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE. Creator An entity primarily responsible for making the resource Ramesh M.; Maheswaran S.; Theivanayaki S.; Kodeeswari K.; Krishnasamy L.; Sriram N. Source A related resource from which the described resource is derived 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCCNT56998.2023.10307852" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCNT56998.2023.10307852</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179839559&amp;doi=10.1109%2FICCCNT56998.2023.10307852&amp;partnerID=40&amp;md5=e0adb3b1e5b898cf056da480adc4a970" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179839559&amp;doi=10.1109%2fICCCNT56998.2023.10307852&amp;partnerID=40&amp;md5=e0adb3b1e5b898cf056da480adc4a970</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033509-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramesh M., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Theivanayaki S., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Kodeeswari K., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Krishnasamy L., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Sriram N., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India