Optimal Emerging trends of Deep Learning Technique for Detection based on Convolutional Neural Network
DOI:
https://doi.org/10.61506/01.00114Keywords:
CT Scans, X-rays, deep learning techniqueAbstract
There has never been a more important need for early, non-invasive lung cancer detection because lung cancer is still one of the world's biggest health concerns. Conventional diagnostic methods such as CT scans and X-rays are very helpful in identifying the disease, but manual interpretation is prone to inconsistent results and human error. In response to this difficulty, our work presents an improved automated approach that uses deep learning models to accurately classify lung images. This work makes use of a large dataset of lung images that have been classified as normal, malignant, and benign. An initial examination of the dataset revealed distinct features related to image dimensions as well as discernible differences between categories. Understanding how important it is for input to neural networks to be consistent, every image was subjected to a thorough preprocessing process in which they were grayscale and standardized to a single dimension. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to address the observed class imbalances within the dataset. Three new architectures—Model I, Model 2, and Model 3—as well as an ensemble method that integrated their forecasts were presented. With an accuracy of roughly 84.7%, Model 1 stood out as the most promising of the models. But the ensemble approach, which was created to capitalize on the advantages of individual models, produced an impressive 82.5% accuracy. Even though Models 2 and 3 had lower accuracy, their distinct advantages and misclassification trends are being taken into consideration for future ensemble enhancements. A prompt, accurate, non-invasive solution to the problems associated with lung cancer detection is provided by the suggested deep learning-driven approach. Reduced diagnostic errors and better patient outcomes could result from its potential for seamless integration with current diagnostic tools. We want to take this research and make it more approachable so that clinicians will accept it and we can move forward with a new generation of diagnostic technology.
References
Adiba, M., Das, T., Paul, A., Das, A., Chakraborty, S., Rosen, M. I., & Nabi, A.H. M. N. (2021). In silico characterization of coding and non-coding SNPs of the androgen receptor gene. Informatics in Medicine Unlocked, 24.
Ahmed, S. F., Alam, M. S. Bin, Afrin, S., Rafa, S. J., Rafa, N., & Gandomi, A.H. (2023). Insights into Internet of Medical Things (loMT): Data fusion, security issues and potential solutions. Information Fusion, 102060.
Akhtar, A., Ch, S. N., & Hussain, M. (2021). In silico computation of functional SNPs of CYP2U I protein leading to hereditary spastic paraplegia. Informatics in Medicine Unlocked, 24.
Akram Ghumman, S., Mahmood, A., Noreen, S., Aslam, A., Ijaz, B., Amanat, A., Kausar, R., Rana, M., & Hameed, H. (2023). Chitosan-Linseed mucilage polyelectrolyte complex nanoparticles of Methotrexate: In vitro cytotoxic efficacy and toxicological studies. Arabian Journal of Chemistry, 16(2).
Alballa, N., & AI-Turaiki, I. (2021). Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. In Informatics in Medicine Unlocked (Vol. 24). Elsevier Ltd.
Al Bassam, N., Hussain, S. A., Al Qaraghuli, A., Khan, J., Sumesh, E. P., & Lavanya, V. (2021). IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19. Informatics in Medicine Unlocked, 24.
Alfarghaly, 0., Khaled, R., Elkorany, A., Reial, M., & Fahmy, A. (2021). Automated radiology report generation using conditioned transformers. Informatics in Medicine Unlocked, 24.
Alharthi, A. M., Lee, M. H., & Algamal, Z. Y. (2021). Gene selection and classification of microarray gene expression data based on a new adaptive LI-norm elastic net penalty. Informatics in Medicine Unlocked, 24.
Almustafa, K. M. (2021). Prediction of chronic kidney disease using different classification algorithms. Informatics in Medicine Unlocked, 24.
Alzain, Z., Alfayez, A., Alsalman, D., Alanezi, F., Hariri, B., Al-Rayes, S., Alhodaib, H., & Alanzi, T. (2021). The role of social media in the training and continuing education of healthcare professionals in Eastern Saudi Arabia. Informatics in Medicine Unlocked, 24.
Amir-Behghadami, M., & Janati, A. (2021). Tele-mental health support community during the COVID-19 pandemic in Iran: Action of State Welfare Organization. In Informatics in Medicine Unlocked (Vol. 24). Elsevier Ltd.
Asadzadeh, A., Samad-Soltani, T., & Rezaei-Hachesu, P. (2021). Applications of virtual and augmented reality in infectious disease epidemics with a focus on the COVID-19 outbreak. In Informatics in Medicine Unlocked (Vol. 24). Elsevier Ltd.
Assistant Professor, S. K. (n.d.). Multi disease Prediction using CNN and Logistic Regression. In Journal of Survey in Fisheries Sciences (Vol. I0, Issue 2S).
Brtiggemann, S., Chan, T., Wardi, G., Mandel, J., Fontanesi, J., & Bitmead, R.R. (2021). Decision support tool for hospital resource allocation during the COVID-19 pandemic. Informatics in Medicine Unlocked, 24.
Bukhari, A., Ijaz, I., Zain, H., Mehmood, U., Mudassir Iqbal, M., Gilani, E., & Nazir, A. (2023). Introduction of CdO nanoparticles into graphene and graphene oxide nanosheets for increasing adsorption capacity of Cr from wastewater collected from petroleum refinery. Arabian Journal of Chemistry, 16(2).
Chakravadhanula, K. (2021). A smartphone-based test and predictive models for rapid, non invasive, and point-of-care monitoring of ocular and cardiovascular complications related to diabetes. Informatics in Medicine Unlocked, 24.
