Evaluating the Efficacy of Convolutional Neural Networks Across Diverse Datasets
DOI:
https://doi.org/10.53560/PPASA(62-1)681Keywords:
Accuracy, Precision, Performance Metrics, Diverse Datasets, CNNsAbstract
Focusing on sentiment analysis and medical image processing, this paper assesses Convolutional Neural Networks performance across different datasets. Emphasized are recent developments in deep learning models for segmentation and picture categorization as well as other purposes. Using contemporary Convolutional Neural Network architectures, this work seeks to get good performance in medical diagnosis and sentiment analysis. The paper emphasizes how well Convolutional Neural Networks perform in domain-specific tasks. Using the same data preparation techniques, appropriate designs, and strategies to split the data, this paper investigates how well Convolutional Neural Network algorithms perform on medical data and sentiment analysis. Convolutional Neural Network models are optimized using hyperparameter tweaking and cross-validation techniques. While guaranteeing patient privacy, data anonymization, and bias reduction, the research seeks to highlight strengths, weaknesses, and patterns. Focusing on ethical concerns and offering suggestions for improvement, it tackles problems in sentiment categorization and medical imaging anomaly detection. This work attains 96 percent accuracy using Convolutional Neural Networks across four datasets. Common measures in the performance assessments of Sentiment Analysis, Skin Cancer Detection, Brain Tumour Detection, and Kidney Stone Detection include F1 scores, recall, and accuracy. With 0.97, Brain Tumour Detection had the highest accuracy; Kidney Stone Detection and Skin Cancer Detection both had 0.95; Sentiment Analysis scored 0.96. The consistently high recall and accuracy scores across all domains indicate good classification capabilities; an F1 score between 0.95 and 0.96 guarantees outstanding performance in both detection and analysis tasks.
References
R.N.V. Jagan Mohan, B.H.V.S. Rama Krishnam Raju, V. Chandra Sekhar, and T.V.K.P. Prasad (Eds.). Algorithms in Advanced Artificial Intelligence. 1st Edition. CRC Press, London, UK (2025).
M. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, and S. Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639): 115-118 (2017).
S.R. Karra and A.K. Kakhandki. An extensive study of facial expression recognition using artificial intelligence techniques with different datasets. Journal of Autonomous Intelligence 6(2): 631 (2023). https://doi.org/10.32629/jai.v6i2.631
A. Khan, S. Ullah, S. Ahmad, and H. Latif. Automated kidney stone detection in CT scans using convolutional neural networks. Journal of Medical Imaging and Health Informatics 11(8): 1980-1989 (2021).
S. Rajendra, K.G. Srinivas, and K.S. Rajasekaran. A Convolutional Neural Network-based framework for brain tumor detection from MRI images. International Journal of Imaging Systems and Technology 31(5): 432-445 (2021).
A. Alrahhal and K. Supreethi. Enhancing CNN performance through content-based image retrieval techniques. International Journal of Computer Vision and Image Processing 12(3): 45-56 (2021).
F.T. Zohra, R.T. Ratri, S. Sultana, and H.B. Rashid. Image segmentation and classification using neural network. International Journal of Computer Science & Information Technology 16(1): 13-27 (2024). https://doi.org/10.5121/ijcsit.2024.16102
R. Silambarasan, K. Venkatesan, and P. Devi. Deep learning approach for the diagnosis of PCOS using medical imaging data. Journal of Medical Imaging and Health Informatics 14(2): 115-124 (2022).
R. Pandiyarajan and M.L. Valarmathi. Dementia classification using VDRNet19: A deep learning model for brain image analysis. Computational Intelligence and Neuroscience 2021: 8854362 (2021).
P. Bandu, T. Ramesh, and N. Shah. Hybrid deep learning models for counterfeit detection in digital transactions. IEEE Transactions on Information Forensics and Security 15: 3021-3030 (2020).
A. Debnath and U.K. Mondal. Leveraging CNN and principal component analysis for dynamic variance control in audio compression. International Journal of Information Technology 17(2): 1039-1047 (2024).
O. Olabanjo, A. Wusu, E. Aigbokhan, O. Olabanjo, O. Afisi, and B. Akinnuwesi. A novel graph convolutional networks model for an intelligent network traffic analysis and classification. International Journal of Information Technology (2025). https://doi.org/10.1007/s41870-024-02032-4
M. Alrahhal and K.P. Supreethi. Integrating machine learning algorithms for robust content-based image retrieval. International Journal of Information Technology 16(8): 4995-5003 (2024).
E. Silambarasan, G. Nirmala, and I. Mishra. Polycystic ovary syndrome detection using optimized SVM and DenseNet. International Journal of Information Technology 17(2): 1039-1047 (2025).
A. Mishra, V.S. Jatti, D.A. Sawant, and A.S. Visave. Novel neurosymbolic artificial intelligence (NSAI) based algorithm to predict specific energy absorption in CoCrMo based architected materials. International Journal of Information Technology 17(2): 999–1005 (2025).
L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M.A. Fadhel, M. Al-Amidie, and L. Farhan. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8(1): 53 (2021).
X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar. A review of convolutional neural networks in computer vision. Artificial Intelligence Review 57: 99 (2024).
S. Cong and Y. Zhou. A review of convolutional neural network architectures and their optimizations. Artificial Intelligence Review 56: 1905-1969 (2023).
I. Rodriguez-Conde, C. Campos, and F. Fdez-Riverola. Optimized convolutional neural network architectures for efficient on-device vision-based object detection. Neural Computing and Applications 34: 10469 (2022).
A. Khan, A. Sohail, U. Zahoora, and A.S. Qureshi. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review 53(8): 5455-5516 (2020).
S.C.D. Bandu, M. Kakileti, S.S.J. Soloman, and N. Baydeti. Indian fake currency detection using image processing and machine learning. International Journal of Information Technology 16(8): 4953-4966 (2024).
C. Sanjay, K. Jahnavi, and S. Karanth. A secured deep learning based smart home automation system. International Journal of Information Technology 16(8): 5239-5245 (2024).
A.C. Tran, B.T. Le, and H.T. Nguyen. Efficiency evaluation of filter sizes on graph convolutional neural networks for information extraction from receipts. International Journal of Information Technology (2025). https://doi.org/10.1007/s41870-024-02089-1
M. Pandiyarajan and R.S. Valarmathi. VDRNet19: a dense residual deep learning model using stochastic gradient descent with momentum optimizer based on VGG-structure for classifying dementia. International Journal of Information Technology (2025). https://doi.org/10.1007/s41870-024-02103-6
A.P. Jaganathan. Meta-styled CNNs: boosting robustness through adaptive learning and style transfer. International Journal of Information Technology (2025). https://doi.org/10.1007/s41870-024-02150-z
K. Mader. Skin Cancer MNIST: HAM10000. Kaggle Datasets (2018). https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000
K. Nawaz, A. Zanib, I. Shabir, J. Li, Y. Wang, T. Mahmood, and A. Rehman. Skin cancer detection using dermoscopic images with convolutional neural network. Scientific Reports 15: 7252 (2025).
O. Sejuti. Axial CT Imaging Dataset-Kidney Stone Detection. Kaggle Datasets (2022). https://www.kaggle.com/datasets/orvile/axial-ct-imaging-dataset-kidney-stone-detection
N. Chakrabarty. Brain MRI Images for Brain Tumor Detection. Kaggle Datasets (2019). https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
Y. Xie, F. Zaccagna, L. Rundo, C. Testa, R. Agati, R. Lodi, D.N. Manners, and C. Tonon. Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): review, challenges, and future perspectives. Diagnostics (Basel) 12(8):1850 (2022).
M. Taus, A. Tasneem, A. Al Mueed, and M.E. Islam. Convolutional Neural Network Based Framework for Classification of Brain Tumor from MRI Images. IEEE 2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 21st - 23rd September 2023, Dhaka, Bangladesh pp. 259-263 (2023). https://doi.org/10.1109/ICICT4SD59951.2023.10303416
S.A.M. Khan. Dataset on Emotion and Facial Detection. Kaggle Datasets (2023). https://www.kaggle.com/datasets/sardarabdulmoizkhan/emotiondetectiondataset
E.S. Agung, A.P. Rifai, and T. Wijayanto. Image-based facial emotion recognition using convolutional neural network on Emognition dataset. Scientific Reports 14: 14429 (2024).
M. Shetty, S.B. Shetty, G.P. Sequeria, and V. Hegde. Kidney Stone Detection Using CNN. 27th Second International Conference on Data Science and Information System (ICDSIS), 17th - 18th May 2024, Hassan, India (2024).
H.A. Khan, W. Jue, M. Mushtaq, and M.U. Mushtaq. Brain tumor classification in MRI image using convolutional neural network. Mathematical Biosciences and Engineering 17(5): 6203-6216 (2020).
D.P. Kingma and J.L. Ba. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR), 7th - 9th May 2015, San Diego, USA (2015). https://arxiv.org/abs/1412.6980.
Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature 521: 436-444 (2015). https://doi.org/10.1038/nature14539.
I. Goodfellow, Y. Bengio, and A. Courville (Eds.). Deep Learning. 1st Edition. MIT Press, Cambridge, MA, USA (2016).
F. Chollet (Ed.). Deep Learning with Python. 1st Edition. Manning Publications, Shelter Island, NY, USA (2017).
R. González-Sendino, E. Serrano, J. Bajo, and P. Novais. A Review of bias and fairness in artificial intelligence. International Journal of Interactive Multimedia and Artificial Intelligence 9(1): 5-17 (2024).
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR), 3rd - 7th May 2021, Virtual Conference (2021). https://arxiv.org/pdf/2010.11929.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 1st - 6th June 2019, Minneapolis, Minnesota, USA (2019). https://arxiv.org/pdf/1810.04805.
B.A. Plummer, L. Wang, C.M. Cervantes, J.C. Caicedo, J. Hockenmaier, and S. Lazebnik. Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models. IEEE International Conference on Computer Vision (ICCV), 11th - 18th December 2015, Santiago, Chile (2015). https://arxiv.org/pdf/1505.04870.
