<![CDATA[Early detection of cervical cancer remains a pivotal strategy to improve clinical outcomes and mitigate mortality associated with this disease. This study introduces a robust deep learning framework employing the ResNet101 architecture to facilitate the automated classification of cervical cell images derived from Pap smear examinations. By leveraging transfer learning, the pre-trained ResNet101 model was fine-tuned to extract salient morphological features critical for distinguishing among diverse cervical cell categories. A comprehensive dataset of labeled Pap smear images, systematically expanded through augmentation techniques, was utilized to enhance model generalizability. The proposed approach achieved a remarkable classification accuracy of 99.6%, highlighting its effectiveness in reliably differentiating between normal and abnormal cellular structures. These findings substantiate the promise of deep residual networks coupled with transfer learning as a powerful tool in advancing computer-aided diagnostic systems, thereby reinforcing early screening initiatives for cervical cancer.]]>
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