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Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана

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188
S. B. Khan et al.
Fig. 7 Balanced kidney pathology dataset
leverages a “cardinality” parameter, enabling it to learn diverse and rich features efciently [26]. By employing grouped convolutions, ResNeXt can capture spatial dependencies effectively and achieve a higher degree of parameter efciency com­pared to traditional CNN models [26]. During training, ResNeXt learns to automati­cally identify and encode complex patterns, textures, and structures within the kidney CT scan images, making it well-suited for medical imaging analysis [25].
In our approach, the ResNeXt model was ne-tuned on the kidney CT scan data­set, which allows it to specialize in recognizing features relevant to kidney abnor­malities. Through multiple convolutional layers and residual connections, the model learned to encode multi-scale information, capturing both low-level and high-level features that are crucial for accurate classication.
3.3.2 Integration ofShapley Values inResNeXt Model forKidney
Tumor Classication
The application of Shapley values [13] holds great potential for understanding how the ResNeXt [25] deep learning model categorizes kidney tumors. By enabling the evaluation of the signicance of individual features that the model relies on to make its predictions, the use of Shapley values [13] enhances the dependability and
Enhancing Diagnosis ofKidney Ailments fromCT Scan withExplainable AI
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Fig. 8 Imbalanced kidney pathology dataset
Fig. 9 ResNeXt framework for image classication
comprehensibility of the model’s results. To calculate the Shapley values, a permutation- based method is employed, as shown in Eq. (10). A trained ResNeXt [25] model for kidney tumor classication is utilized, and the Shapley value for each feature is determined by systematically altering its values while maintaining the other features as constant. This process is repeated for every potential feature
190
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combination, and the average of their marginal contributions is calculated, resulting in the establishment of the Shapley value for each feature.
S. B. Khan et al.
f
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where ϕi(f) is the Shapley value of feature i in function f, N represents the set of all features, S is a subset of features that does not include feature i. f(S) represents the model’s prediction when considering the subset S. f(S∪{i}) denotes the model’s prediction when including feature i in the subset S. Upon calculating the Shapley values for all features, they can be incorporated into the ResNeXt [25] model frame­work, resulting in visualizations such as bar charts or heatmaps that illustrate the signicance of each feature. This methodology enables clinicians and stakeholders to gain insight into which features played the most substantial role in the model’s predictions, providing an explanation and justication for those predictions.
3.3.3 Classication: Integrating Grad-CAM forInterpretability
Our research team harnessed advanced technologies, including the feature sets from ResNeXt [25] and XAI models, to precisely classify kidney abnormalities into three distinct categories: cysts, stones, and tumors. Our approach prioritized the creation of robust and interpretable predictions, explicitly focusing on enhancing the trans­parency of complex AI models [27] (Fig.10).
In particular, we delved into the realm of AI Shapley values. This method enabled us to quantify the inuence of each feature within the input data on the resulting prediction [28]. Analyzing the Shapley values associated with each CT scan image, we identied specic regions within the scans that played pivotal roles in the mod­el’s classication decisions. To further augment our predictions’ interpretability, we leveraged heatmaps generated through AI Shapley values. These heatmaps offered a visual depiction of the critical areas within the kidney CT scans that the model
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Fig. 10 XAI Grad-CAM architecture
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Enhancing Diagnosis ofKidney Ailments fromCT Scan withExplainable AI
191
relied upon for its decision-making process. This visual representation not only bol­stered the condence of medical practitioners in the model’s predictions but also provided them with a means to validate the outcomes against their medical expertise and experience [27].
3.4 Visual Explanations Using Grad-CAM
We used a method called gradient-weighted class activation mapping (Grad-CAM) to better understand how the ResNeXt [25] model makes decisions. This technique helps pinpoint important areas in input images that inuenced the model’s predic­tions. With Grad-CAM’s heatmaps, we were able to identify critical sections within kidney CT scans [27] and determine exactly which regions inuenced the model’s categorization decisions. We applied Grad-CAM to representative scans for each class —cyst, normal, stone, and tumor —and compared the resulting heatmaps to the original CT scans for a side-by-side comparison (Fig.11).
Here is the steps involved in the Grad-CAM function:
• Let f(x) be the output of the nal convolutional layer of the model for an input
image x, and
• Calculate the gradient of
feature map at position (i, j) in the output of the nal convolutional layer. This
step computes the importance weights αk(i, j) for each activation. This gradient
can be represented as in Eq. (11):
be the predicted class of x.
with respect to the activations A(k, i, j) of the k-th
Fig. 11 The complete process for Grad-CAM analysis for kidney stone, cyst, tumor, and nor­mal classes
192
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S. B. Khan et al.
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• Calculate the global average pooling of the gradients αk(i, j) to obtain the impor-
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• Calculate the weighted sum of the feature maps using the importance weights
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• Resize the heatmap Lk to match the size of the input image x using interpolation,
resulting in the nal Grad-CAM heatmap H∧k.
• Normalize the heatmap H∧k to have values between 0 and 1, ensuring that the
heatmap highlights the regions that were most important for the CNN’s decision.
The Grad-CAM heatmap H∧k is a helpful tool that visually highlights the regions within kidney CT scan images that inuenced the model’s classication decisions. This transparency into the decision-making process enhances the interpretability and trustworthiness of deep learning model predictions. Medical professionals can use these visual explanations to identify specic features or patterns that led to the model’s classication, improving diagnostic condence and informed treatment decisions.
In Fig.13, we can see the Grad-CAM output for kidney cyst classication. The heatmap in the Grad-CAM explanation image highlights areas that correspond to cysts, suggesting that the model relied on these regions to make its cyst predictions. On the other hand, the second, third, and fourth explanations display few red or blue pixels, which means that the chances of the input images being classied as normal, stone, or tumor are decreased.
In Fig.14, the Grad-CAM output for kidney stones is shown. The heatmap dis­plays red pixels that are focused on areas that correspond to kidney stones. This indicates that the model has correctly identied these regions and the image is cat­egorized as showing kidney stones.
The Grad-CAM output for normal kidneys is displayed in Fig.15. The heatmap generated by Grad-CAM exhibits minimal red or blue pixels, which indicates that there are no abnormalities detected in the image by the model. Consequently, the image is classied as a normal kidney. Grad-CAM is an exceptional tool that pro­vides visual explanations to aid clinicians in comprehending the regions within kid­ney CT scan images that affected the model’s classication decisions. By offering
Enhancing Diagnosis ofKidney Ailments fromCT Scan withExplainable AI
transparency into the decision-making process, Grad-CAM enhances the interpret­ability and reliability of the deep learning model’s predictions. Medical professionals can now identify the particular features or patterns that led to the model’s classica­tion, leading to improved diagnostic condence and informed treatment decisions. These visual explanations are of immense value to medical professionals.
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4 Results andDiscussion
We employed advanced techniques, namely Shapley values and Grad-CAM, to comprehensively assess the reliability and accuracy of the ResNeXt [25] model. Our evaluation process involved a thorough examination of various metrics. We closely monitored the model’s loss and accuracy for the training set. In addition, we relied on well-established metrics, including precision, F1-score, and recall, for the validation and test sets. These metrics served as essential benchmarks to ensure that the model’s predictions were more than merely a result of memorizing the training data, which could lead to overtting. We utilized distinct validation and test datasets to gauge the model’s ability to generalize and make accurate predictions on new data. The training of the ResNeXt [25] model was carried out meticulously, employ­ing a tenfold cross-validation strategy spanning 15 epochs. To prevent overtraining, we skill fully integrated an early stopping mechanism through callbacks.
The results were auspicious, with the model achieving an average training accu­racy of 99.30% and consistently maintaining a high validation accuracy of 99.39%. When subjected to independent data for testing, the model exhibited exceptional prociency, achieving an average testing accuracy of 99.52% and minimal testing loss, as low as 0.0291. These ndings are visually represented in Fig.12, which showcases the training and validation loss values at 0.0557 and 0.0491, respectively.
Our assessment conrms that the ResNeXt model [25] is a reliable and accurate tool for predicting outcomes. To further evaluate its discriminative capacity between positive and negative samples, we constructed a receiver operating characteristic (ROC) curve for the tenthfold, as depicted in Fig.12. The area under the curve (AUC) for this ROC curve impressively reached 99.52% for all abnormalities, underscoring the model’s precision in distinguishing between the two classes. This remarkable AUC value was complemented by a noteworthy true positive rate (TPR), further reinforcing the model’s effectiveness in correctly identifying positive instances. The ResNeXt model’s [25] results highlight its efciency in accurately categorizing kidney-related anomalies, even when dealing with datasets exhibiting irregular sample distributions. The proposed model’s high accuracy and reliability make it valuable for streamlining diagnostic and therapeutic processes related to kidney disorders in healthcare assistance.
In order to gain a deeper understanding of the ResNeXt [25] model’s mathemati­cal behavior, XAI techniques were employed. Grad-CAM results were presented for each category (cyst, normal, stone, tumor), with the corresponding testing images showcased in Figs.13, 14, and 15, alongside their respective heatmaps. The
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S. B. Khan et al.
Fig. 12 Output of Grad-CAM XAI for kidneys and cysts
Grad-CAM explanation image in Fig.13 highlights the regions of the image that increase the probability of predicting a cyst. The likelihood of classifying the input image as normal, stone, or tumor decreased since the second, third, and fourth explanations contained no red or blue pixels. In Fig.14, the red pixels concentrated
Enhancing Diagnosis ofKidney Ailments fromCT Scan withExplainable AI
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Fig. 13 Output of Grad-CAM XAI for kidneys and kidney stones
on the stone image, indicating it was normal, whereas in Fig.15, it was determined to be a normal kidney.
To validate the proposed framework’s superiority over the traditional CNN approach, an extensive evaluation was conducted, and various statistics and visual­izations were obtained, as illustrated in Fig.16. The accuracy of both models was compared on the test set, with the proposed ResNeXt model achieving an
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S. B. Khan et al.
Fig. 14 Output of Grad-CAM XAI for kidneys
impressive accuracy of 99.52%, while the traditional CNN model attained an accu­racy of 95.80%. The proposed framework outperforms the traditional approach regarding accuracy.
Moving on to precision, recall, and F1-score described in Table 2, signicant differences were found between the two models. The proposed ResNeXt model
Enhancing Diagnosis ofKidney Ailments fromCT Scan withExplainable AI
Fig. 15 Accuracy, loss, and ROC curve analysis of ResNeXt model for training and validation set
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Fig. 16 Comparison of proposed work with simple CNN model with 500 epochs
Table 2 Model evaluation metrics
Model Accuracy (%) Precision (%) Recall (%) F1-score (%) ResNeXt 99.52 99.35 99.53 99.44 Traditional CNN 95.80 95.82 95.67 95.75
demonstrated higher precision (0.9935) than the traditional CNN (0.9582). Similarly, the recall for the ResNeXt model (0.9953) surpassed that of the CNN model (0.9567). The F1-score, which reects the model’s overall accuracy, was also higher for the proposed ResNeXt model (0.9944) compared to the traditional CNN model (0.9575). These metrics indicate that the proposed framework achieves better classication results.
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