DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence

Wani, Niyaz Ahmad; Kumar, Ravinder; Bedi, Jatin

doi:10.1016/j.cmpb.2023.107879

Cited by 43 publications

(3 citation statements)

References 45 publications

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“…The present study aligns with prior research on diverse domains, which includes heartbeat classification [39], lung disease prediction [34], face image analysis, Parkinson's disease diagnosis [40], lung cancer detection [41], brain tumour [42] and skin disease identification [43]. The results demonstrate that the accuracy scores surpassed 90 percent.…”

Section: Originalsupporting

confidence: 78%

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning” at Evolving Systems.

Nwokoro,

Akinnuwesi,

Shastri

et al. 2024

Preprint

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The significance of maternal health cannot be overemphasized, and the ability to predict maternal outcomes accurately is critical to ensuring the well-being of both mothers and infants. This study presents ConvXGB, a novel predictive model that utilizes a combination of XGBoost, a potent gradient boosting algorithm, and deep learning to extract intricate features. The objective is to enhance precision and robustness of maternal outcome predictions. The study sourced diverse maternal health data from the southern region of Nigeria and implemented Synthetic Minority Over-sampling Technique (SMOTE) to address any dataset imbalances. Results obtain demonstrate a significant improvement in model performance, with an accuracy rate of 97.96% across various maternal outcome classes. The recommendations from this study highlight the potential of ConvXGB in advancing maternal health predictive analytics, supporting informed clinical decision-making, and improving resource allocation. Further studies are warranted to explore the broader applicability of ConvXGB in different healthcare domains through outcome analyses and methodological advancements.

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Section: Originalsupporting

confidence: 78%

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning” at Evolving Systems.

Nwokoro,

Akinnuwesi,

Shastri

et al. 2024

Preprint

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show abstract

“…Point (B) will presumably create a shift in paradigm, giving place to a revolution [49] both in philosophy and neurosciences and giving birth to a kind of neuro-philosophy [86][87][88] that sees the collaboration between different research as its main strength.…”

Section: Discussionmentioning

confidence: 99%

Problems of Connectionism

Vassallo,

Sattin,

Parati

et al. 2024

Philosophies

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The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism’s problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core.

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“…The high computational complexity caused by the attention mechanisms poses challenges for deploying the proposed method in real-time clinical medical treatment systems. Additionally, considering the stringent requirements for patient privacy and data security in medical applications, enhancing the interpretability and transparency of our model is essential for gaining the acceptance and trust of doctors [51,52].…”

Section: Discussionmentioning

confidence: 99%

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer

Wang,

He,

Liu

et al. 2024

Sensors

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The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.

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DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence

Cited by 43 publications

References 45 publications

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning” at Evolving Systems.

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning” at Evolving Systems.

Problems of Connectionism

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer

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