Slika omota

Image-Based Deep Learning for Brain Tumour Transcriptomics: A Benchmark of DeepInsight, Fotomics, and Saliency-Guided CNNs

Spremljeno u:
Izdano u:Machine Learning and Knowledge Extraction vol. 7, no. 4 (2025), p. 119-134
Glavni autor: Alyatimi Ali
Daljnji autori: Chung, Vera, Iqbal, Muhammad Atif, Anaissi Ali
Izdano:
MDPI AG
Teme:
Datasets
Accuracy
Tumors
Gene expression
Deep learning
Classification
Brain cancer
Fourier transforms
Salience
Artificial neural networks
Neural networks
Glioma
Quality control
Brain
Images
Algorithms
Machine learning
Benchmarks
Online pristup:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Oznake: Dodaj oznaku
Bez oznaka, Budi prvi tko označuje ovaj zapis!
Sažetak:Classifying brain tumour transcriptomic data is crucial for precision medicine but remains challenging due to high dimensionality and limited interpretability of conventional models. This study benchmarks three image-based deep learning approaches, DeepInsight, Fotomics, and a novel saliency-guided convolutional neural network (CNN), for transcriptomic classification. DeepInsight utilises dimensionality reduction to spatially arrange gene features, while Fotomics applies Fourier transforms to encode expression patterns into structured images. The proposed method transforms each single-cell gene expression profile into an RGB image using PCA, UMAP, or t-SNE, enabling CNNs such as ResNet to learn spatially organised molecular features. Gradient-based saliency maps are employed to highlight gene regions most influential in model predictions. Evaluation is conducted on two biologically and technologically different datasets: single-cell RNA-seq from glioblastoma GSM3828672 and bulk microarray data from medulloblastoma GSE85217. Outcomes demonstrate that image-based deep learning methods, particularly those incorporating saliency guidance, provide a robust and interpretable framework for uncovering biologically meaningful patterns in complex high-dimensional omics data. For instance, ResNet-18 achieved the highest accuracy of 97.25% on the GSE85217 dataset and 91.02% on GSM3828672, respectively, outperforming other baseline models across multiple metrics.
ISSN:2504-4990
Digitalni identifikator objekta:10.3390/make7040119
Izvor:Advanced Technologies & Aerospace Database