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MedSpectralNet: A lightweight convolutional neural network architecture for multi-modal image classification.

Nabilah Afrin¹, Masud An-Nur Islam Fahim¹, Wasan Alamro²

¹Innovative Skills Ltd., Dhaka, Bangladesh.

|April 27, 2026

Summary

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Classification of Signals

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This summary is machine-generated.

MedSpectralNet, a new lightweight Convolutional Neural Network (CNN), efficiently classifies medical images by extracting multi-frequency features. It achieves high accuracy with fewer parameters, making it ideal for real-time applications.

Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Medical image classification demands models balancing local patterns, global structures, and computational efficiency.
State-of-the-art models like MedMamba use State-Space Models (SSMs) but face limitations in parallelism and runtime due to sequential operations.

Purpose of the Study:

To introduce MedSpectralNet, a lightweight Convolutional Neural Network (CNN) architecture.
To overcome the limitations of sequential models by enabling efficient extraction of multi-frequency features with linear complexity.
To provide a computationally efficient and parallelizable solution for medical image classification.

Main Methods:

Developed MedSpectralNet, a lightweight CNN approximating self-attention with linear complexity.

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Implemented a dual-stream feature extractor for parallel processing of global and local information.

Introduced a ContextGate block for adaptive fusion of multi-scale representations.

Main Results:

Achieved high accuracy across six MedMNIST benchmark datasets, including 93.7% on OrganCMNIST and 98.0% on BloodMNIST.
Demonstrated relative accuracy gains of 1-4.3% over larger transformer-based models.
Achieved high Area Under the Curve (AUC) values up to 0.999 across multiple classes.

Conclusions:

MedSpectralNet offers state-of-the-art accuracy with substantially reduced computational cost (8.5 million parameters) and improved parallelization.
The model's efficiency and performance make it well-suited for real-time and resource-constrained medical image classification applications.
MedSpectralNet presents a viable alternative to existing models for clinical deployment.