Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches

[1] § Research design › Soft voting-based ensemble classification ↔ model.py, lines 831–887 · score 0.63 · Transformer backbone, soft voting, GNN, SAE, DBN, classification
[2] § Research design › Cross attention-based feature extraction ↔ model.py, lines 262–350 · score 0.57 · feed forward, layer normalization, residual, Cross, model
[3] § Performance assessment ↔ evaluation.py, lines 280–325 · score 0.53 · AUC score, F1 score, recall, precision, accuracy

Paper

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The authors' code

Python · 1,002 lines · 19 KB · no license · 2 matches

# ==========================================================
# CNN BACKBONE
# ==========================================================
import torch
import torch.nn as nn
class CNNBackbone(nn.Module):
def __init__(self):
super().__init__()
self.features = nn.Sequential(
# ----------------------------------
# BLOCK 1
# ----------------------------------
nn.Conv2d(
3,
64,
kernel_size=3,
padding=1
),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
# 224 -> 112
# ----------------------------------
# BLOCK 2
# ----------------------------------
nn.Conv2d(
64,
128,
kernel_size=3,
padding=1
),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
# 112 -> 56
# ----------------------------------
# BLOCK 3
# ----------------------------------
nn.Conv2d(
128,
256,
kernel_size=3,
padding=1
),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
# 56 -> 28
# ----------------------------------
# BLOCK 4
# ----------------------------------
nn.Conv2d(
256,
512,
kernel_size=3,
padding=1
),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
# 28 -> 14
# ----------------------------------
# BLOCK 5
# ----------------------------------
nn.Conv2d(
512,
768,
kernel_size=3,
padding=1
),
nn.BatchNorm2d(768),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.features(x)
return x
# ==========================================================
# TRANSFORMER BACKBONE
# ==========================================================
import torch
import torch.nn as nn
class TransformerBackbone(nn.Module):
def __init__(
self,
image_size=224,
patch_size=16,
embed_dim=768,
depth=6,
num_heads=8
):
super().__init__()
self.image_size = image_size
self.patch_size = patch_size
self.embed_dim = embed_dim
# ----------------------------------
# PATCH EMBEDDING
# ----------------------------------
self.patch_embed = nn.Conv2d(
in_channels=3,
out_channels=embed_dim,
kernel_size=patch_size,
stride=patch_size
)
num_patches = (
image_size // patch_size
) ** 2
# ----------------------------------
# CLS TOKEN
# ----------------------------------
self.cls_token = nn.Parameter(
torch.zeros(
1,
1,
embed_dim
)
)
# ----------------------------------
# POSITION EMBEDDING
# ----------------------------------
self.pos_embed = nn.Parameter(
torch.zeros(
1,
num_patches + 1,
embed_dim
)
)
# ----------------------------------
# TRANSFORMER ENCODER
# ----------------------------------
encoder_layer = nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
dim_feedforward=embed_dim * 4,
dropout=0.1,
activation="gelu",
batch_first=True
)
self.transformer = nn.TransformerEncoder(
encoder_layer,
num_layers=depth
)
self.norm = nn.LayerNorm(
embed_dim
)
def forward(self, x):
# ----------------------------------
# PATCH EMBEDDING
# ----------------------------------
x = self.patch_embed(x)
# [B,768,14,14]
B = x.shape[0]
x = x.flatten(2)
# [B,768,196]
x = x.transpose(1, 2)
# [B,196,768]
# ----------------------------------
# CLS TOKEN
# ----------------------------------
cls_tokens = self.cls_token.expand(
B,
-1,
-1
)
x = torch.cat(
[cls_tokens, x],
dim=1
)
# [B,197,768]
# ----------------------------------
# POSITION EMBEDDING
# ----------------------------------
x = x + self.pos_embed
# ----------------------------------
# TRANSFORMER
# ----------------------------------
x = self.transformer(x)
x = self.norm(x)
return x
# ==========================================================
# CROSS ATTENTION
# ==========================================================
import torch
import torch.nn as nn
class CrossAttention(nn.Module):
def __init__(
self,
embed_dim=768,
num_heads=8
):
super().__init__()
self.attention = nn.MultiheadAttention(
embed_dim=embed_dim,
num_heads=num_heads,
batch_first=True
)
self.norm1 = nn.LayerNorm(
embed_dim
)
self.norm2 = nn.LayerNorm(
embed_dim
)
self.mlp = nn.Sequential(
nn.Linear(
embed_dim,
embed_dim * 4
),
nn.GELU(),
nn.Linear(
embed_dim * 4,
embed_dim
)
)
def forward(
self,
transformer_features,
cnn_features
):
# ----------------------------------
# QUERY
# ----------------------------------
query = transformer_features
# ----------------------------------
# KEY / VALUE
# ----------------------------------
key = cnn_features
value = cnn_features
# ----------------------------------
# CROSS ATTENTION
# ----------------------------------
attended_features, attention_weights = \
self.attention(
query=query,
key=key,
value=value
)
# ----------------------------------
# RESIDUAL CONNECTION
# ----------------------------------
x = transformer_features + attended_features
x = self.norm1(x)
# ----------------------------------
# FEED FORWARD
# ----------------------------------
mlp_output = self.mlp(x)
x = x + mlp_output
x = self.norm2(x)
return x
# ==========================================================
# GNN LAYER
# ==========================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphLayer(nn.Module):
def __init__(
self,
in_features,
out_features
):
super().__init__()
self.linear = nn.Linear(
in_features,
out_features
)
def forward(
self,
x,
adj
):
x = torch.bmm(
adj,
x
)
x = self.linear(x)
x = F.relu(x)
return x
# ==========================================================
# GNN MODULE
# ==========================================================
class GNNModule(nn.Module):
def __init__(
self,
input_dim=768,
hidden_dim=512,
output_dim=256
):
super().__init__()
self.gnn1 = GraphLayer(
input_dim,
hidden_dim
)
self.gnn2 = GraphLayer(
hidden_dim,
output_dim
)
def build_adjacency(
self,
batch_size,
num_nodes,
device
):
adj = torch.eye(
num_nodes,
device=device
)
adj = adj.unsqueeze(0)
adj = adj.repeat(
batch_size,
1,
1
)
return adj
def forward(
self,
x
):
# x
# [B,197,768]
B, N, _ = x.shape
adj = self.build_adjacency(
B,
N,
x.device
)
x = self.gnn1(
x,
adj
)
x = self.gnn2(
x,
adj
)
return x
# ==========================================================
# RBM LAYER
# ==========================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
class RBMLayer(nn.Module):
def __init__(
self,
visible_units,
hidden_units
):
super().__init__()
self.fc = nn.Linear(
visible_units,
hidden_units
)
def forward(self, x):
x = self.fc(x)
x = torch.sigmoid(x)
return x
# ==========================================================
# DBN MODULE
# ==========================================================
class DBNModule(nn.Module):
def __init__(self):
super().__init__()
self.rbm1 = RBMLayer(
visible_units=256,
hidden_units=512
)
self.rbm2 = RBMLayer(
visible_units=512,
hidden_units=256
)
self.rbm3 = RBMLayer(
visible_units=256,
hidden_units=128
)
def forward(self, x):
# -----------------------------
# [B,197,256]
# -----------------------------
x = self.rbm1(x)
# [B,197,512]
x = self.rbm2(x)
# [B,197,256]
x = self.rbm3(x)
# [B,197,128]
return x
# ==========================================================
# ENCODER
# ==========================================================
import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(
self,
input_dim,
latent_dim
):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(
input_dim,
latent_dim
),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.encoder(x)
# ==========================================================
# DECODER
# ==========================================================
class Decoder(nn.Module):
def __init__(
self,
latent_dim,
output_dim
):
super().__init__()
self.decoder = nn.Sequential(
nn.Linear(
latent_dim,
output_dim
),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.decoder(x)
# ==========================================================
# SAE MODULE
# ==========================================================
class SAEModule(nn.Module):
def __init__(self):
super().__init__()
# -------------------------
# ENCODERS
# -------------------------
self.encoder1 = Encoder(
128,
64
)
self.encoder2 = Encoder(
64,
32
)
# -------------------------
# DECODERS
# -------------------------
self.decoder1 = Decoder(
32,
64
)
self.decoder2 = Decoder(
64,
128
)
def forward(self, x):
# -------------------------
# ENCODING
# -------------------------
x = self.encoder1(x)
# [B,197,64]
latent = self.encoder2(x)
# [B,197,32]
# -------------------------
# DECODING
# -------------------------
x = self.decoder1(latent)
x = self.decoder2(x)
reconstruction = x
return latent, reconstruction
# ==========================================================
# BRANCH CLASSIFIER
# ==========================================================
import torch
import torch.nn as nn
class BranchClassifier(nn.Module):
def __init__(
self,
input_dim,
num_classes
):
super().__init__()
self.classifier = nn.Sequential(
nn.Linear(
input_dim,
128
),
nn.ReLU(inplace=True),
nn.Dropout(0.3),
nn.Linear(
128,
num_classes
)
)
def forward(self, x):
return self.classifier(x)
# ==========================================================
# SOFT VOTING
# ==========================================================
class SoftVotingClassifier(nn.Module):
def __init__(
self,
num_classes
):
super().__init__()
# ------------------------------
# GNN BRANCH
# ------------------------------
self.gnn_classifier = BranchClassifier(
input_dim=256,
num_classes=num_classes
)
# ------------------------------
# DBN BRANCH
# ------------------------------
self.dbn_classifier = BranchClassifier(
input_dim=128,
num_classes=num_classes
)
# ------------------------------
# SAE BRANCH
# ------------------------------
self.sae_classifier = BranchClassifier(
input_dim=32,
num_classes=num_classes
)
def forward(
self,
gnn_features,
dbn_features,
sae_features
):
# --------------------------------
# GLOBAL AVERAGE TOKEN POOLING
# --------------------------------
gnn_features = gnn_features.mean(
dim=1
)
dbn_features = dbn_features.mean(
dim=1
)
sae_features = sae_features.mean(
dim=1
)
# --------------------------------
# INDIVIDUAL BRANCH PREDICTIONS
# --------------------------------
gnn_logits = self.gnn_classifier(
gnn_features
)
dbn_logits = self.dbn_classifier(
dbn_features
)
sae_logits = self.sae_classifier(
sae_features
)
# --------------------------------
# SOFTMAX PROBABILITIES
# --------------------------------
gnn_prob = torch.softmax(
gnn_logits,
dim=1
)
dbn_prob = torch.softmax(
dbn_logits,
dim=1
)
sae_prob = torch.softmax(
sae_logits,
dim=1
)
# --------------------------------
# SOFT VOTING
# --------------------------------
final_prob = (
gnn_prob +
dbn_prob +
sae_prob
) / 3.0
return (
final_prob,
gnn_logits,
dbn_logits,
sae_logits
)
# ==========================================================
# CAXDT-MLCRND
# ==========================================================
class CAXDT_MLCRND(nn.Module):
def __init__(
self,
num_classes=40
):
super().__init__()
# ----------------------------------
# CNN
# ----------------------------------
self.cnn = CNNBackbone()
# ----------------------------------
# TRANSFORMER
# ----------------------------------
self.transformer = TransformerBackbone()
# ----------------------------------
# CROSS ATTENTION
# ----------------------------------
self.cross_attention = CrossAttention(
embed_dim=768,
num_heads=8
)
# ----------------------------------
# GNN
# ----------------------------------
self.gnn = GNNModule(
input_dim=768,
hidden_dim=512,
output_dim=256
)
# ----------------------------------
# DBN
# ----------------------------------
self.dbn = DBNModule()
# ----------------------------------
# SAE
# ----------------------------------
self.sae = SAEModule()
# ----------------------------------
# SOFT VOTING
# ----------------------------------
self.soft_voting = SoftVotingClassifier(
num_classes=num_classes
)
def forward(self, x):
# ==================================================
# CNN
# ==================================================
cnn_features = self.cnn(x)
# [B,768,14,14]
cnn_tokens = cnn_features.flatten(2)
cnn_tokens = cnn_tokens.transpose(
1,
2
)
# [B,196,768]
# ==================================================
# TRANSFORMER
# ==================================================
transformer_features = self.transformer(x)
# [B,197,768]
# ==================================================
# CROSS ATTENTION
# ==================================================
fused_features = self.cross_attention(
transformer_features,
cnn_tokens
)
# [B,197,768]
# ==================================================
# GNN
# ==================================================
gnn_features = self.gnn(
fused_features
)
# [B,197,256]
# ==================================================
# DBN
# ==================================================
dbn_features = self.dbn(
gnn_features
)
# [B,197,128]
# ==================================================
# SAE
# ==================================================
sae_features, reconstruction = \
self.sae(
dbn_features
)
# sae_features
# [B,197,32]
# ==================================================
# SOFT VOTING
# ==================================================
(
final_prob,
gnn_logits,
dbn_logits,
sae_logits
) = self.soft_voting(
gnn_features,
dbn_features,
sae_features
)
return {
"probabilities":
final_prob,
"gnn_logits":
gnn_logits,
"dbn_logits":
dbn_logits,
"sae_logits":
sae_logits,
"reconstruction":
reconstruction,
"cnn_features":
cnn_features,
"transformer_features":
transformer_features,
"fused_features":
fused_features
}

model.py at commit bd0645b, no license · at the source

Overview

Authors: Preethi Veerlapalli¹, E. Laxmi Lydia², K. Radhika³, Kancherla R. Krishna Naga Surya Alekhya⁴, Faten Khalid Karim⁵, Mohamad Khairi Ishak⁶, Samih M. Mostafa⁷

Department of Computer Science and Engineering, Malla Reddy University,Hyderabad, India
Department of Computer Science and Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, 530046 Andhra Pradesh India
AI&DS Department, Chaitanya Bharathi Institute of Technology,Gandipet, 500075 Hyderabad India
Department of Computer Science and Engineering, Aditya University,Surampalem, India
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University,P.O. Box 84428, Riyadh, 11671 Saudi Arabia
Department of Computer Engineering, College of Computing and Informatics, University of Sharjah,Sharjah, United Arab Emirates
Computer Science Department, Faculty of Computers and Information, Qena University, Qena, 83523 Egypt

Institutions: Malla Reddy University (India); Chaitanya Bharathi Institute of Technology (India); Aditya University (India); Princess Nourah bint Abdulrahman University (Saudi Arabia); University of Sharjah (United Arab Emirates); South Valley University (Egypt)

Journal: Scientific reports, volume 16, issue 1, article 19357

Dates: received 22 March 2026; accepted 15 June 2026; published online 22 June 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41598-026-58491-1 · PMID 42332136 · PMCID PMC13287759 · OpenAlex W7165575620

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: structural MRI / diffusion (modality), human (organism)

Methods: Machine learning, Statistics, Physiology & signal measures

Keywords: Rare neurological disorder, Deep learning, Bayesian optimization, Explainable artificial intelligence, Magnetic resonance imaging, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neurology, Neuroscience

MeSH: Deep Learning*, Magnetic Resonance Imaging*, Nervous System Diseases*, Convolutional Neural Networks, Humans (* major topic)

Topic: Explainable Artificial Intelligence (XAI) (Artificial Intelligence, Computer Science), according to OpenAlex

Funding: Princess Nourah Bint Abdulrahman University (PNURSP2026R300)

Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

researcher010-debug/NeurologicalDisorderClassification

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: bd0645b2e710894a1dff33eb810c79a8b0d98f3e, 10 June 2026

Languages: Python (5)

Size: 6 files, 5 scripts

Software Heritage: not archived

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: PyTorch (4 files), NumPy (3 files), Pillow (3 files), OpenCV (2 files), scikit-learn (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

5 files

evaluation.py, Python, 425 lines, 1 match
model.py, Python, 1,002 lines, 2 matches
preprocess.py, Python, 152 lines
train.py, Python, 471 lines
visualization.py, Python, 252 lines

At the source: github.com/researcher010-debug/NeurologicalDisorderClassification.git

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: researcher010-debug/NeurologicalDisorderClassification

Read it in the paper: doi.org/10.1038/s41598-026-58491-1.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
5 scripts, each with its path and the digest of its content;
3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

data.mendeley.com/datasets/d73rs38yk6, at Mendeley Data; found in “Data availability”

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: data.mendeley.com/datasets/d73rs38yk6

Read it in the paper: doi.org/10.1038/s41598-026-58491-1.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 10 keywords, 5 MeSH terms, 1 funder, 33 references.

Cite

This paper

Veerlapalli, P., Laxmi Lydia, E., Radhika, K., Surya Alekhya, K. R. K. N., Karim, F. K., Ishak, M. K., & Mostafa, S. M. (2026). Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images. Scientific reports, 16(1), 19357. https://doi.org/10.1038/s41598-026-58491-1

BibTeX

@article{veerlapalli2026cross, author = {Veerlapalli, Preethi and Laxmi Lydia, E. and Radhika, K. and Surya Alekhya, Kancherla R. Krishna Naga and Karim, Faten Khalid and Ishak, Mohamad Khairi and Mostafa, Samih M.}, title = {{Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images}}, journal = {Scientific reports}, year = {2026}, month = jun, volume = {16}, number = {1}, pages = {19357}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-58491-1}, url = {https://doi.org/10.1038/s41598-026-58491-1}, pmid = {42332136}, pmcid = {PMC13287759} }

RIS

TY - JOUR AU - Veerlapalli, Preethi AU - Laxmi Lydia, E. AU - Radhika, K. AU - Surya Alekhya, Kancherla R. Krishna Naga AU - Karim, Faten Khalid AU - Ishak, Mohamad Khairi AU - Mostafa, Samih M. TI - Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/06/22 VL - 16 IS - 1 SP - 19357 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-58491-1 UR - https://doi.org/10.1038/s41598-026-58491-1 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41598-026-58491-1", "type": "article-journal", "title": "Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images", "container-title": "Scientific reports", "author": [ { "family": "Veerlapalli", "given": "Preethi" }, { "family": "Laxmi Lydia", "given": "E." }, { "family": "Radhika", "given": "K." }, { "family": "Surya Alekhya", "given": "Kancherla R. Krishna Naga" }, { "family": "Karim", "given": "Faten Khalid" }, { "family": "Ishak", "given": "Mohamad Khairi" }, { "family": "Mostafa", "given": "Samih M." } ], "container-title-short": "Sci Rep", "volume": "16", "issue": "1", "page": "19357", "DOI": "10.1038/s41598-026-58491-1", "PMID": "42332136", "PMCID": "PMC13287759", "ISSN": "2045-2322", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41598-026-58491-1", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 22 ] ] } }

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In common: OpenCV, Pillow, PyTorch, 2 other tools
[9] doi:10.1038/s41598-026-61605-4 [code]: Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.
Journal: Scientific reports
In common: OpenCV, Pillow, PyTorch, 2 other tools
[10] doi:10.1038/s41467-026-76837-1 [code]: Drug screen and machine learning predict neuroprotective agents in a preclinical human model of childhood dementia.
Journal: Nature communications
In common: OpenCV, Pillow, PyTorch, 2 other tools

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