arXiv:2511.13877v1 Announce Type: new 
Abstract: This paper proposes a new enhanced model architecture to perform classification of lumbar spine degeneration with DICOM images while using a hybrid approach, integrating EfficientNet and VGG19 together with custom-designed components. The proposed model is differentiated from traditional transfer learning methods as it incorporates a Pseudo-Newton Boosting layer along with a Sparsity-Induced Feature Reduction Layer that forms a multi-tiered framework, further improving feature selection and representation. The Pseudo-Newton Boosting layer makes smart variations of feature weights, with more detailed anatomical features, which are mostly left out in a transfer learning setup. In addition, the Sparsity-Induced Layer removes redundancy for learned features, producing lean yet robust representations for pathology in the lumbar spine. This architecture is novel as it overcomes the constraints in the traditional transfer learning approach, especially in the high-dimensional context of medical images, and achieves a significant performance boost, reaching a precision of 0.9, recall of 0.861, F1 score of 0.88, loss of 0.18, and an accuracy of 88.1%, compared to the baseline model, EfficientNet. This work will present the architectures, preprocessing pipeline, and experimental results. The results contribute to the development of automated diagnostic tools for medical images.

تقدم هذه الورقة نموذجًا معماريًا جديدًا لتصنيف تدهور العمود الفقري القطني باستخدام صور DICOM. يجمع هذا النهج الهجين بين EfficientNet و VGG19 مع مكونات مصممة خصيصًا، مما يميزه عن طرق التعلم بالنقل التقليدية. يتضمن طبقة تعزيز بوساطة نيوتن الزائفة التي تعزز من تنويعات أوزان الميزات وطبقة تقليل الميزات المستندة إلى الندرة التي تقلل من التكرار في الميزات المتعلمة. تهدف هذه البنية الجديدة إلى تحسين تمثيل الميزات التشريحية التي غالبًا ما يتم تجاهلها في الإعدادات التقليدية، مما يؤدي إلى اكتشاف أكثر دقة للمرض.

El artículo presenta una nueva arquitectura de modelo para clasificar la degeneración de la columna lumbar utilizando imágenes DICOM. Este enfoque híbrido combina EfficientNet y VGG19 con componentes personalizados, diferenciándose de los métodos tradicionales de aprendizaje por transferencia. Incluye una capa de refuerzo Pseudo-Newton que mejora las variaciones de los pesos de las características y una capa de reducción de características inducida por la escasez que minimiza la redundancia en las características aprendidas. Esta arquitectura innovadora busca mejorar la representación de carac…

Cet article propose une nouvelle architecture de modèle pour classer la dégénérescence de la colonne lombaire à l'aide d'images DICOM. Cette approche hybride combine EfficientNet et VGG19 avec des composants personnalisés, la distinguant des méthodes traditionnelles d'apprentissage par transfert. Elle comprend une couche de renforcement Pseudo-Newton qui améliore les variations des poids des caractéristiques et une couche de réduction de caractéristiques induite par la parcimonie qui minimise la redondance des caractéristiques apprises. Cette architecture innovante vise à améliorer la représen…

The paper presents a novel model architecture for classifying lumbar spine degeneration using DICOM images. This hybrid approach combines EfficientNet and VGG19 with custom components, distinguishing it from traditional transfer learning methods. It features a Pseudo-Newton Boosting layer that enhances feature weight variations and a Sparsity-Induced Feature Reduction Layer that minimizes redundancy in learned features. This innovative architecture aims to improve the representation of anatomical features often overlooked in conventional setups, leading to more accurate pathology detection.

Hybrid Convolution Neural Network Integrated with Pseudo-Newton Boosting for Lumbar Spine Degeneration Detection

arXiv:2601.08457v1 Announce Type: cross 
Abstract: Digital platforms have an ever-expanding user base, and act as a hub for communication, business, and connectivity. However, this has also allowed for the spread of hate speech and misogyny. Artificial intelligence models have emerged as an effective solution for countering online hate speech but are under explored for low resource and code-mixed languages and suffer from a lack of interpretability. Explainable Artificial Intelligence (XAI) can enhance transparency in the decisions of deep learning models, which is crucial for a sensitive domain such as hate speech detection. In this paper, we present a multi-modal and explainable web application for detecting misogyny in text and memes in code-mixed Hindi and English. The system leverages state-of-the-art transformer-based models that support multilingual and multimodal settings. For text-based misogyny identification, the system utilizes XLM-RoBERTa (XLM-R) and multilingual Bidirectional Encoder Representations from Transformers (mBERT) on a dataset of approximately 4,193 comments. For multimodal misogyny identification from memes, the system utilizes mBERT + EfficientNet, and mBERT + ResNET trained on a dataset of approximately 4,218 memes. It also provides feature importance scores using explainability techniques including Shapley Additive Values (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). The application aims to serve as a tool for both researchers and content moderators, to promote further research in the field, combat gender based digital violence, and ensure a safe digital space. The system has been evaluated using human evaluators who provided their responses on Chatbot Usability Questionnaire (CUQ) and User Experience Questionnaire (UEQ) to determine overall usability.

قدمت دراسة جديدة تطبيقًا ويبًا متعدد الوسائط وقابلًا للتفسير مصممًا لاكتشاف كراهية النساء في اللغة الهندية والإنجليزية المختلطة، باستخدام نماذج متقدمة من الذكاء الاصطناعي مثل XLM-RoBERTa. يهدف هذا التطبيق إلى تعزيز قابلية تفسير اكتشاف خطاب الكراهية، وهو أمر حاسم في سياق تزايد كراهية النساء عبر الإنترنت.

Un nuevo estudio ha presentado una aplicación web multimodal y explicable diseñada para detectar la misoginia en un código mezclado de hindi e inglés, utilizando modelos avanzados de inteligencia artificial como XLM-RoBERTa. Esta aplicación tiene como objetivo mejorar la interpretabilidad de la detección de discursos de odio, lo cual es crucial en el contexto del creciente problema de la misoginia en línea.

Une nouvelle étude a présenté une application web multimodale et explicable conçue pour détecter la misogynie dans un mélange de hindi et d'anglais, utilisant des modèles avancés d'intelligence artificielle tels que XLM-RoBERTa. Cette application vise à améliorer l'interprétabilité de la détection des discours de haine, ce qui est crucial dans le contexte de la misogynie en ligne croissante.

A new study has introduced a multimodal and explainable web application designed to detect misogyny in code-mixed Hindi and English, utilizing advanced artificial intelligence models like XLM-RoBERTa. This application aims to enhance the interpretability of hate speech detection, which is crucial in the context of increasing online misogyny.

Hybrid Convolution Neural Network Integrated with Pseudo-Newton Boosting for Lumbar Spine Degeneration Detection

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