Automated Cervical Cancer Detection through Visual Inspection with Acetic Acid in Resource-Poor Settings with Lightweight Deep Learning Models Deployed on an Android Device

• URL: http://arxiv.org/abs/2508.13253v1
• Date: Mon, 18 Aug 2025 14:44:51 GMT
• Title: Automated Cervical Cancer Detection through Visual Inspection with Acetic Acid in Resource-Poor Settings with Lightweight Deep Learning Models Deployed on an Android Device
• Authors: Leander Melroy Maben, Keerthana Prasad, Shyamala Guruvare, Vidya Kudva, P C Siddalingaswamy,
• Abstract summary: We propose a lightweight deep learning algorithm that includes EfficientDet-Lite3 as the Region of Interest (ROI) detector and a MobileNet- V2 based model for classification.<n>These models would be deployed on an android-based device that can operate remotely and provide almost instant results.<n>The classification model gives an accuracy of 92.31%, a sensitivity of 98.24%, and a specificity of 88.37% on the test dataset.
• Score: 0.13194391758295113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cervical cancer is among the most commonly occurring cancer among women and claims a huge number of lives in low and middle-income countries despite being relatively easy to treat. Several studies have shown that public screening programs can bring down cervical cancer incidence and mortality rates significantly. While several screening tests are available, visual inspection with acetic acid (VIA) presents itself as the most viable option for low-resource settings due to the affordability and simplicity of performing the test. VIA requires a trained medical professional to interpret the test and is subjective in nature. Automating VIA using AI eliminates subjectivity and would allow shifting of the task to less trained health workers. Task shifting with AI would help further expedite screening programs in low-resource settings. In our work, we propose a lightweight deep learning algorithm that includes EfficientDet-Lite3 as the Region of Interest (ROI) detector and a MobileNet- V2 based model for classification. These models would be deployed on an android-based device that can operate remotely and provide almost instant results without the requirement of highly-trained medical professionals, labs, sophisticated infrastructure, or internet connectivity. The classification model gives an accuracy of 92.31%, a sensitivity of 98.24%, and a specificity of 88.37% on the test dataset and presents itself as a promising automated low-resource screening approach.

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