arXiv:2511.18083v1 Announce Type: new 
Abstract: Background and Objective: Deep learning models have high computational needs and lack interpretability but are often the first choice for medical image classification tasks. This study addresses whether complex neural networks are essential for the simple binary classification task of malaria. We introduce the Extracted Morphological Feature Engineered (EMFE) pipeline, a transparent, reproducible, and low compute machine learning approach tailored explicitly for simple cell morphology, designed to achieve deep learning performance levels on a simple CPU only setup with the practical aim of real world deployment.
  Methods: The study used the NIH Malaria Cell Images dataset, with two features extracted from each cell image: the number of non background pixels and the number of holes within the cell. Logistic Regression and Random Forest were compared against ResNet18, DenseNet121, MobileNetV2, and EfficientNet across accuracy, model size, and CPU inference time. An ensemble model was created by combining Logistic Regression and Random Forests to achieve higher accuracy while retaining efficiency.
  Results: The single variable Logistic Regression model achieved a test accuracy of 94.80 percent with a file size of 1.2 kB and negligible inference latency (2.3 ms). The two stage ensemble improved accuracy to 97.15 percent. In contrast, the deep learning methods require 13.6 MB to 44.7 MB of storage and show significantly higher inference times (68 ms).
  Conclusion: This study shows that a compact feature engineering approach can produce clinically meaningful classification performance while offering gains in transparency, reproducibility, speed, and deployment feasibility. The proposed pipeline demonstrates that simple interpretable features paired with lightweight models can serve as a practical diagnostic solution for environments with limited computational resources.

تقدم دراسة جديدة إطار عمل EMFE (محرك الميزات المورفولوجية المستخرجة) كنهج خفيف الوزن للتعلم الآلي لتصنيف الملاريا، والذي يحقق مستويات أداء مقارنة بنماذج التعلم العميق بينما يتطلب طاقة حسابية أقل بكثير. تستخدم هذه الطريقة مجموعة بيانات NIH Malaria Cell Images، مع التركيز على ميزات بسيطة لمورفولوجيا الخلايا مثل عدد البكسلات غير الخلفية وعدد الثقوب داخل الخلايا.

Un nuevo estudio presenta el pipeline Extracted Morphological Feature Engineered (EMFE), un enfoque de aprendizaje automático ligero para la clasificación de la malaria que logra niveles de rendimiento comparables a los modelos de aprendizaje profundo, pero requiere significativamente menos potencia computacional. Este método utiliza el conjunto de datos NIH Malaria Cell Images, centrándose en características simples de morfología celular, como los píxeles no de fondo y los agujeros dentro de las células.

Une nouvelle étude présente le pipeline Extracted Morphological Feature Engineered (EMFE), une approche d'apprentissage automatique légère pour la classification du paludisme, qui atteint des niveaux de performance comparables à ceux des modèles d'apprentissage profond tout en nécessitant beaucoup moins de puissance de calcul. Cette méthode utilise le jeu de données NIH Malaria Cell Images, en se concentrant sur des caractéristiques simples de morphologie cellulaire telles que les pixels non arrière-plan et les trous dans les cellules.

A new study introduces the Extracted Morphological Feature Engineered (EMFE) pipeline, a lightweight machine learning approach for malaria classification that achieves performance levels comparable to deep learning models while requiring significantly less computational power. This method utilizes the NIH Malaria Cell Images dataset, focusing on simple cell morphology features such as non-background pixels and holes within cells.

Less Is More: An Explainable AI Framework for Lightweight Malaria Classification

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.

An Under-Explored Application for Explainable Multimodal Misogyny Detection in code-mixed Hindi-English

One More Thing in AI – Your Shortcut to AI Mastery

Less Is More: An Explainable AI Framework for Lightweight Malaria Classification

Was this article worth reading? Share it

One More Thing in AI

LucidQuery AI

Chattermate

Attentive AI

FastML

The Visualizer

Ready to build your own newsroom?