Refining Pancreatic Cancer Detection and Classification Through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis

• Published in: 2024 Second International Conference on Advances in Information Technology (ICAIT) Vol. 1; pp. 1 - 6
• Main Authors: ME, P Arunachalam, Devi, M. Kalpana, Jose, Naduvathezhath Nessariose, G, Vivekanandan, Sharma, Rinku Mahesh, Yadav, Ganesh Kumar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24-07-2024
• Subjects: Accuracy; Classification algorithms; Deep learning; Feature extraction; Hybrid Approach; Image analysis; Medical diagnostic imaging; Medical Image Analysis; Optimization; Pancreatic cancer; Refinement; Sensitivity and specificity; Sparrow Search Algorithm; Stacked Deep Learning; Training
• DOI: 10.1109/ICAIT61638.2024.10690435

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learning with the Sparrow Search Algorithm unveils a new epoch in the medical image analysis and the treatment of the patient, which proves to be a major advancement in the improvement of classical methods for pancreatic cancer classification.