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All Journal Jurnal Pendidikan Ilmu Sosial Prosiding Seminar Nasional Pakar Asia Pacific Fraud Journal SAINSTECH: Jurnal Penelitian dan Pengkajian Sains dan Teknologi Advance Sustainable Science, Engineering and Technology (ASSET)
INDRA KUSUMA
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Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

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Optimizing Image Preprocessing for AI-Driven Cervical Cancer Diagnosis Chandra Prasetyo Utomo; Neng Suhaeni; Nashuha Insani; Elan Suherlan; Nunung Ainur Rahmah; Ahmad Rusdan Utomo; Indra Kusuma; Muhamad Fathurachman; Dewa Nyoman Murti Adyaksa
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1128

Abstract

Cervical cancer ranks among the top causes of cancer-related deaths in women globally. Early detection is vital for improving patient survival rates. The multiclass classification of cervical cell images presents challenges primarily due to the notable variations in cell sizes across different classes. Conventional AI methods for diagnosing cervical cancer often rely on image-resizing techniques that overlook crucial features like relative cell dimensions, which impairs the models' ability to distinguish between classes effectively. This paper presents a novel AI-driven approach that employs constant padding to maintain the natural size differences among cells. Our method utilizes deep learning for both feature extraction and multiclass classification. We assessed the method using the publicly accessible SIPaKMeD dataset. Experimental findings indicate that our approach surpasses traditional image-resizing methods, especially in classes that are more challenging to predict. This strategy highlights AI's potential to improve cervical cancer diagnosis, offering a more precise and dependable tool for early detection. A reliable and precise AI model for diagnosing cervical cancer is crucial for promoting widespread screening and ensuring timely and effective treatment, which can ultimately lower mortality rates. By aiding early and accurate diagnosis, this approach aligns with global health efforts to alleviate the burden of cancer and other diseases, especially in areas with limited access to advanced healthcare services facilities.
Co-Authors Abdining Ageng Ahmad Rusdan Utomo Chandra Prasetyo Utomo Dewa Nyoman Murti Adyaksa Elan Suherlan Halim Dedy Perdana Komariah Komariah Muhamad Fathurachman Nashuha Insani Neng Suhaeni Nunung Ainur Rahmah Sapto Mei Budiyanto Sri Suranta