Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

Mahjoubi, Mohamed Amine; Hamida, Soufiane; Gannour, Oussama El; Cherradi, Bouchaib; Abbassi, Ahmed El; Raihani, Abdelhadi

doi:10.14569/ijacsa.2023.0140346

Cited by 30 publications

(6 citation statements)

References 27 publications

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“…Utilizing various network architectures, such as Convolutional Neural Networks (CNN) [11], [12], an increasing body of research has been dedicated to applying DL techniques to classify skin diseases [13]. This literature review offers a comprehensive overview of recent studies that have utilized DL techniques to classify skin diseases and highlights their contributions to the field [14], [15].…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

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In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.

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Section: Literature Reviewmentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

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“…Medical image understanding using CNN has shown promising results in various medical domains, including disease classification, tumor segmentation, lesion detection, identifying anatomical location [50,[134][135][136] and diagnosing COVID-19 and metastatic cancer with high classification accuracy [137]. Overall, the use of CNNs in medical image classification has significant implications for clinical practice, including improving the accuracy and speed of diagnosis, and for treatment planning.…”

Section: Implications For Clinical Practicementioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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“…High-level characteristics from previous levels are used to construct final predictions in these layers. Backpropagation is used to change the weights of neurons in a DNN to minimize the discrepancy between anticipated and actual output [24]. Optimization using stochastic gradient descent [25].…”

Section: Deep Nn Algorithmmentioning

confidence: 99%

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

Hamida,

Lamrani,

El Gannour

et al. 2024

Bulletin EEI

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Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.

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Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

Cited by 30 publications

References 27 publications

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

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