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178
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Related works on explainable articial intelligence (XAI) for clinical decision-making
Table 1
and diagnosis
Disease
category Disease
Cancer Breast cancer Holzinger
Lung cancer Lundervold
Prostate
cancer
Cardiovascular
disease
Neurological
disorders
Cancer Skin cancer Selvaraju
Cardiovascular
disease
Alzheimer’s
disease
Parkinson’s
disease
Colorectal
cancer
Brain tumor Schlemper
Atrial
brillation
Coronary
artery
disease
Research
paper
etal. (2019)
Al’Aref etal.
(2020)
etal. (2019)
Yoon etal.
(2020)
Azizi etal.
(2020)
Goldenberg
etal. (2021)
Chen etal.
(2020)
Ribeiro etal.
(2021)
Zhou etal.
(2020)
Boers etal.
(2021)
Saadi etal.
(2020)
Kim etal.
(2021)
etal. (2017)
Bychkov
etal. (2018)
etal. (2019)
Hannun etal.
(2019)
Attia etal.
(2019)
Reference
no.
[1] LRP Histopathology image
[2] SHAP Predicting breast cancer
[3]
[4] LIME Histopathology image
[5] SHAP Predicting prostate
[6] LIME Prostate cancer
[7] LRP
[8] SHAP Predicting major
[9] LRP Predicting disease
[10] SHAP Predicting Alzheimer’s
[11] LIME Predicting response to
[12]
[13]
[14] LRP Histopathology image
[15] LRP Brain tumor
[16]
[17] SHAP Predicting coronary
XAI
technique Application
classication
recurrence
GradCAM
GradCAM
GradCAM
GradCAM
Lung nodule
classication in CT
scans
classication
cancer recurrence
detection on
multiparametric MRI
Echocardiographybased heart failure
prediction
adverse cardiac events
progression based on
brain MRI
disease progression
from MRI data
deep brain stimulation
Parkinson’s disease
diagnosis based on gait
analysis data
Skin lesion
classication from
dermoscopic images
classication
segmentation in MRI
Detection of atrial
brillation from ECG
signals
artery disease from
clinical data
S. B. Khan et al.
(continued)
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Table 1 (continued)
Disease
category Disease
Neurological
disorders
Infectious
diseases
Rare and
complex
diseases
Epilepsy Roy etal.
Multiple
sclerosis
Malaria Rajaraman
Pneumonia Rahman etal.
Diabetic
retinopathy
Cystic
brosis
Research
paper
(2019)
Ribeiro etal.
(2020)
etal. (2018)
(2020)
Poplin etal.
(2018)
Ronneberger
etal. (2015)
Reference
no.
[18] LIME Seizure prediction from
[19] SHAP Lesion segmentation in
[20]
[21] LIME Pneumonia detection
[22]
[23] U-Net Lung segmentation and
XAI
technique Application
EEG signals
MRI
GradCAM
GradCAM
Malaria parasite
detection in blood
smear images
from chest X-ray
images
Diabetic retinopathy
detection from retinal
images
analysis in CT scans
179
to develop a model for classifying the spatial structure of lobular carcinoma in situ
from histopathology images related to lung cancer.
Other research works have been using advanced AI and machine learning techniques to make signicant strides in the ght against prostate cancer. For instance,
Azizi and colleagues (2020) utilized deep transfer learning and SHAP values to
analyze choline and spermine levels and predict cancer recurrence. Meanwhile,
Goldenberg etal. (2021) have enumerated the breakthroughs in AI and machine
learning research pertaining to prostate cancer. They found that LIME is an effective
tool for identifying anomalies in multiparametric MRI scans. Chen and his team
(2020) also contributed to the eld by providing an insightful critique of the
advancements in deep learning for segmenting cardiac images. They emphasized
the signicance of LRP in forecasting heart failures based on echocardiography.
Finally, Ribeiro etal. (2021) introduced CardioNet, a state-of-the-art deep learning
algorithm for predicting the health trajectories of individuals who have undergone
SARS-CoV-2 RT-PCR tests. Their model also uses SHAP values to help explain its
predictions.
Researchers in the eld of neurological ailments have been exploring the use of
deep learning to analyze medical ultrasounds. Zhou and their team (2020) have
focused on using LRP to forecast the progression of Alzheimer’s disease based on
MRI scans of the brain. Meanwhile, Boers and their colleagues (2021) have conducted a comprehensive review on imaging indicators for heart failure patients,
including the use of SHAP values to predict Alzheimer’s disease progression
through MRI insights. Saadi and their group (2020) have employed explainable
deep learning to anticipate patient responses to deep brain stimulation treatments
for Parkinson’s disease, utilizing LIME to elucidate their model’s decision-making
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Fig. 1 Research distribution of XAI on disease diagnosis and prognosis
S. B. Khan et al.
Fig. 2 AI techniques research distribution on disease diagnosis and prognosis
process. Similarly, Kim and associates (2021) have implemented a deep learningdriven approach to analyzing the walking patterns of Parkinson’s patients, integrating explainable AI using Grad-CAM to enhance the transparency of its predictive
outcomes. These research efforts highlight the potential of explainable AI tools in
improving diagnostic and prognostic processes across various medical specialties.
Figure 1 distributions indicate that explainable AI techniques are being actively
researched and applied across various diseases, focusing on cancer, cardiovascular
disease, and neurological disorders. However, these percentages are only approximations and may not represent the exact distribution of publications. The eld of
explainable AI in healthcare is constantly evolving, with new research and applications emerging over time.
In healthcare, different AI techniques, namely decision trees (DT), support vector machines (SVM), logistic regression (LR), neural networks (NN), convolutional
neural networks (CNN), LIME, SHAP, and Grad-CAM and its research distribution
are shown in Fig.2.
xx
′
−
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2.1 XAI Techniques inMedical Diagnosis
Explainable AI (XAI) techniques provide transparency and interpretability in disease diagnosis and prognosis models. These methods help elucidate the decisionmaking process of AI models, fostering trust and promoting the adoption of AI in
healthcare settings. Some commonly used XAI techniques in disease diagnosis and
prognosis are discussed in the following subsections.
2.1.1 LIME (Local Interpretable Model-Agnostic Explanations)
The LIME (Local Interpretable Model-agnostic Explanations) method is a technique employed in the eld of explainable articial intelligence (XAI) to provide
clear and understandable insights into the predictions generated by complex
machine learning models, regardless of their complexity. The fundamental concept
of LIME is to simulate the behavior of a complex model, denoted as “f,” in the local
context of a particular instance, “x,” that requires an explanation. This simulation
comprises several crucial stages.
First, LIME generates a set of altered instances based on the original instance,
“x,” by adding noise, modifying features, or using domain-specic techniques to
diversify the data. Second, the complex model, “f,” predicts the output for each
altered instance, resulting in a set of input–output pairs. Third, each altered instance
is assigned weights based on its similarity to the original instance, “x.” A kernel
function, such as the exponential kernel, is utilized to calculate the weights, taking
into account the Euclidean distance between instances and a bandwidth parameter,
“σ,” represented by the following equation:
Here, “x” and “x′” denote two instances, ||x– x′|| represents the Euclidean distance
between them, and “σ” is the bandwidth parameter. Fourth, an interpretable model,
usually linear, is tted to the altered instances using the assigned weights. Weighted
least squares or other relevant optimization techniques are employed for this purpose. Lastly, the tted interpretable model, represented as “g,” explains the original
instance, “x,” in feature contributions. In the case of a linear model, these contributions are calculated as the product of the model’s coefcients and the corresponding
feature values.
By following these steps, LIME generates an interpretable model, “g,” that mimics the behavior of the complex model, “f,” in the local vicinity of instance “x.” The
feature contributions derived from the interpretable model provide a deeper understanding of the decision-making process of the complex model and valuable insights
into the factors that inuence the prediction for a specic instance, “x.” Consequently,
the LIME method can be used to enhance the transparency and interpretability of
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S. B. Khan et al.
complex machine learning models, thereby facilitating their adoption in various
business and academic settings.
2.1.2 SHAP (SHapley Additive exPlanations)
SHAP, which stands for Shapley Additive exPlanations, is a signicant advancement in explainable AI (XAI). It assesses the importance of features in various
machine learning models. SHAP uses cooperative game theory and Shapley values
to determine the impact of each feature on the nal prediction. These values provide
a detailed view of the model’s behavior by identifying the critical features for individual predictions. In the context of SHAP, let’s consider a complex model “f,” an
explanation instance “x,” and the corresponding prediction “f(x).” SHAP aims to
allocate the prediction value fairly among the features used by the model. To achieve
this, it uses Shapley values derived from cooperative game theory principles. Here,
a “game” involves a group of participants or features and a characteristic function,
v, that assigns a value to each possible combination of participants. In this instance,
the characteristic function, v(S), is equal to the model’s prediction, f(xS), when only
the features within the coalition, S, are considered, as seen in Eq. (2):
(2)
Here, xS represents the features in set S. The Shapley value assigned to a particular
feature, I, denoted as φi, represents the average contribution of that feature across all
possible combinations, as shown in Eq. (3):
SN S
−−
!!
i
SNi
⊆∑/
1
∪
vS ivS=
·
N
!
−
()
(3)
Here, N represents the complete set of features, |S| is the cardinality of set S, and |N|
is the total count of features. The Shapley values of all features in instance x can be
represented as a vector, as shown in Eq. (4):
,,,
(4)
The SHAP value associated with a feature indicates its impact on the prediction,
f(x), compared to the expected prediction, which corresponds to the mean prediction
resulting from all possible feature combinations, as expressed in Eq. (5):
f xEfX
()=()
+
i
(5)
Here, E[f(X)] represents the expected value of the prediction. It is crucial to note that
computing precise Shapley values can be computationally challenging, particularly
for high-dimensional datasets or complex models. Therefore, to efciently compute
ijk
L A
k
kk
=∗
∑
α
H L=
()
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SHAP values for a wide range of model types, several approximation techniques,
such as KernelSHAP, TreeSHAP, and DeepSHAP, have been developed. By providing a consistent and interpretable measure of feature importance, SHAP values signicantly enhance our understanding of the decision-making processes within
complex models, providing valuable insights into the factors that shape predictions
for specic instances.
2.1.3 Grad-CAM (Gradient-Weighted Class Activation Mapping)
The Grad-CAM (Gradient-weighted Class Activation Mapping) technique is an
explainable AI method designed explicitly for convolutional neural networks
(CNNs). Its purpose is visually highlighting the regions in an input image crucial
for the network’s classication decision [15]. By generating heatmaps that bring
attention to the most relevant areas in the image, it becomes easier to comprehend
the model’s focus and reasoning. The Grad-CAM approach aims to generate a heatmap highlighting the image’s essential regions for the predicted class c, given a
CNN model f and an input image x. To achieve this, Grad-CAM follows a specic
set of steps.
• Identify the target layer: Grad-CAM focuses on the last convolutional layer of
the CNN, which contains the high-level features that are most relevant to the
classication task. Let A be the activation map of this layer with dimensions
H×W, where H and W are the height and width of the map, respectively.
• Compute the gradients: Calculate the gradients of the score for the predicted
class c (denoted as Yc) with respect to the activation map A. The gradients
(∂Yc/∂A) represent the importance of each activation for the predicted class.
• Calculate the weights α: Compute the weights α by global average pooling the
gradients over the height and width dimensions (6):
• where k is the index of the k-th feature map, and A
(i, j) of the k-th feature map.
• Compute the weighted activation map: Multiply each feature map in A by its cor-
responding weight α_k, and sum the weighted feature maps to obtain the
weighted activation map L (7):
• Generate the heatmap: Apply a ReLU function to the weighted activation map L
to eliminate the negative values and obtain the nal heatmap (8):
W
∂
k
=
1
HW
∗
∗
∑∑
iHj
==
11
Y
c
∂
A
,,
is the activation at location
i, j, k
(6)
(7)
ReLU
(8)
184
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+∗
()−()
λ
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• The resulting heatmap H highlights the regions in the input image that contrib-
uted the most to the predicted class c. Grad-CAM can provide insights into the
model’s decision-making process, enabling users to identify potential biases,
verify the model’s focus on relevant features, and ensure that the model is not
relying on irrelevant or spurious patterns.
Grad-CAM is designed explicitly for CNNs and may not apply to other types of
neural networks or machine learning models. However, it has been widely used for
explainability in image classication tasks and can be adapted for other tasks such
as object detection or semantic segmentation.
2.1.4 Counterfactual
Counterfactual explanations play a pivotal role in articial intelligence by shedding
light on the decision-making processes of machine learning models. They identify
the subtlest changes to the input data that could lead to an alternative outcome, thus
presenting multiple scenarios capable of altering a model’s output. This approach
proves invaluable when dissecting intricate models, as it aids in pinpointing the key
determinants inuencing their predictions. Moreover, counterfactual explanations
serve as a crucial instrument for model validation, error detection, and the acquisition of profound insights into the fundamental factors driving AI-driven decisions.
S. B. Khan et al.
• Dene an objective function: The objective function quanties the distance
between the original instance x and the counterfactual x′, and the difference in
the predictions f(x) and f(x′). The objective function can be represented as in
Eq. (9):
• where d(x, x′) is a distance metric between the instances (e.g., Euclidean distance
or Manhattan distance) and λ is a regularization parameter that balances the
two terms.
• Optimization: The goal is to minimize the objective function L(x, x′) by nding
an instance x′ that is close to x but leads to a different prediction. This can be
achieved using various optimization techniques, such as gradient descent, genetic
algorithms, or Bayesian optimization.
• Interpret the counterfactual: The counterfactual instance x′ provides an explana-
tion for the original instance x by highlighting the smallest changes that need to
be made to x in order to obtain a different prediction.
These changes can help users understand the factors that the model considers
important for the given decision. By generating counterfactual instances, counterfactual explanations provide interpretable insights into the model’s decision- making
(9)
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process, enabling users to identify the specic features and their changes that would
lead to different outcomes. This can help in understanding the model’s behavior,
validating its decisions, and identifying potential biases or errors in the model’s
predictions.
2.1.5 Rule-Based Explanations
Rule-based explanations are a category of explainable AI methods that provide
transparent and interpretable insights into a model’s decisions. These methods
dene explicit rules or conditions that map input features to model predictions.
Unlike complex black box models, rule-based explanations offer a clear and understandable set of guidelines for why a particular decision was reached. These rules
include “if–then” statements, decision trees, or logical expressions. Rule-based
explanations are precious when transparency and interpretability are essential, such
as in medical diagnoses, nance, or legal applications. They allow users to trace the
model’s decision process step-by-step, making validating its outputs easier, identifying biases, and gaining trust in AI systems.
Rule extraction: The rule deducing procedure is contingent on both the model
kind and the selected rule portrayal. For instance:
– Neural networks might be approximated via decision trees.
– Linear models could be translated into logical stipulations.
Rule extraction methodologies are typically divided into:
• Intrinsic: Direct rule extraction from the inherent model structure is achieved
here. Instances include the induction of decision trees or deducing rules from
linear models.
• Post hoc: Here, the model’s behavior is approximated through a separate, com-
prehensible model such as decision trees or association rules. Renowned post
hoc rule extraction techniques encompass the C4.5 algorithm (decision tree
induction), RIPPER (rule induction), and LASSO (for linear models).
• Rule interpretation: The rules, once derived as R, help in elucidating the predic-
tion f(x) for the instance x. This is achieved by pinpointing the precise conditions
precipitating the decision. Rules can be exemplied as logical propositions (e.g.,
IF feature1 exceeds threshold1 AND feature2 is below threshold2, THEN the
class is deemed positive) or could be presented in a decision tree format, portray-
ing the decision trajectory.
Rule-based explanations, by presenting an intelligible set of rules reecting the
model’s operations, allow users to gain deeper insights into the determinants that
mold predictions. This fosters trust in the model, aids in discerning potential biases
or inaccuracies, and offers constructive guidance for users, enabling them to rene
the model or make well-informed judgments.
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3 Classication ofKidney Diseases Using Grad-CAM
3.1 Data Collection
The dataset for this study was collected from multiple hospitals in Dhaka,
Bangladesh, using the picture archiving and communication system (PACS) [24].
The dataset includes patients who have been diagnosed with kidney tumors, cysts,
normal cases, or stones. Both the coronal and axial cuts were selected from both
contrast and noncontrast studies, following the protocol for the whole abdomen
and urogram. The DICOM studies were selected carefully, one diagnosis at a
time, and then converted to a batch of DICOM images (Fig.3) for each radiologi-
cal nding. The patient information and metadata were excluded from the DICOM
images, which were then converted to lossless JPEG format. A radiologist and a
medical technologist veried each image nding to ensure data correctness
(Figs.3, 4, 5 and 6).
Fig. 3 Sample kidney CT scan image slices with stone as classication
Fig. 4 Sample kidney’s CT scan images with normal category as classication
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Fig. 5 Sample kidney CT scan image with tumor as a classication
Fig. 6 Sample kidney CT scan image with cyst as a classication
187
3.2 Data Preprocessing
The dataset containing categories with labels of normal, cyst, and stone is imbalanced, as the count of each category is not equal. The normal category has the highest count with 5077, followed by cyst with 3709, and the stone, tumor category with
3660 (2283, 1377) shown in Fig. 7. However, the SMOTE algorithm has been
applied to balance the dataset by generating synthetic data points for the minority
class. After applying the SMOTE algorithm, the dataset is balanced all categories
shown in Fig.8.
3.3 Feature Extraction andClassication
3.3.1 Feature Extraction: Deep Feature Learning (ResNeXt)
In this study, we employed advanced deep feature learning techniques to extract
discriminative and informative features from the kidney CT scan images. Deep
learning has demonstrated remarkable success in various image-related tasks due to
its ability to learn hierarchical representations from raw data. For feature extraction,
we utilized a state-of-the-art convolutional neural network (CNN) architecture
known as ResNeXt [25] shown in Fig.9. ResNeXt is a deep CNN model that
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