# Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging

arXiv: https://arxiv.org/abs/2402.16221 · HTML: https://mlsanity.com/paper/2402.16221/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [Dataset Distillation in Medical Imaging: A Feasibility Study](https://mlsanity.com/paper/2407.14429/) (2024)
- [Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection](https://mlsanity.com/paper/2509.05190/) (2025)
- [Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images](https://mlsanity.com/paper/2603.28357/) (2026)

## Abstract (arXiv)

This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection, followed by developing and training a CNN model for accurate classification. Various image processing techniques, including Gaussian smoothing, bilateral filtering, and K-means clustering, are employed to preprocess the input images and highlight tumor regions. The CNN model is trained and evaluated on a dataset of medical images, with augmentation and data generators utilized to enhance model generalization. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting tumors in medical images, paving the way for improved diagnostic tools in healthcare.