# A scalable and reliable deep learning framework for enhanced brain tumor detection and diagnosis using AI-based medical imaging

## TL;DR

This paper introduces a two-stage deep learning framework for detecting brain tumors in MRI scans, achieving high accuracy and reliability for clinical use.

## Contribution

A novel two-stage deep learning framework combining segmentation and classification for brain tumor detection with strong clinical applicability.

## Key findings

- The proposed framework achieved 99.31% classification accuracy using the ADAM optimizer.
- Segmentation before classification improved detection reliability compared to single-stage models.
- The framework is robust, interpretable, and suitable for secure clinical environments.

## Abstract

The proposed Architecture will provide the processing and analysis essential to accurate and reliable detection of brain tumors from MRI, for timely diagnosis and evidence-based decisions. Medical imaging now routinely enters clinical assessment; the thrust is shifting toward attaining high performance within open and governed systems to enable deployment in real-world healthcare applications.

This paper proposes a two-stage deep learning framework: first, DeepLabV3 for segmentation to demarcate candidate tumor regions, and then CNN to classify whether a tumor exists. The different components employ pre-trained models through transfer learning and fine-tuning. The DeepLabV3 and CNN architectures are used, together with metric computation modules. This approach will be tested on BraTS MRI data. For efficient model training, optimizers such as SGD, RMSprop, and Adam can be employed.

The classification performance could be achieved with a high value of 99.31% using an ADAM optimizer in the proposed architecture. Besides, both the precision and recall are very high, indicating good generalization and stable performance. Moreover, segmenting before classification provides more reliable detection compared to using a single-stage model.

These results indicate that feature learning guided by segmentation enhances tumor detection with a binary classifier, while remaining interpretable and robust. This makes the framework much more transparent and easy to audit, suitable for use in cloud-enabled, secure, and IoT-enabled clinical environments.

It therefore proposes a two-layer deep learning architecture that effectively incorporates precise tumor localization into explicit binary tumor detection. Beyond this, the work focuses on practical clinical applicability, robust data governance, and deployment-ready systems rather than diagnosing subtypes of tumors.