LAUTECH Journal of Engineering and Technology

Development of Brain Tumor Classification System using Convolutional Neural Network Model with Explainable Artificial Intelligence

T. H. Stephen — 2026-07-17

Brain tumors are abnormal cell growths in the brain that require accurate and timely diagnosis, where Magnetic Resonance Imaging (MRI) plays a critical role in detecting and characterizing tumor structures. However, accurate interpretation of Magnetic Resonance Imaging (MRI) scans is challenging due to their complexity and the limited availability of expert radiologists. This challenge is further compounded by the lack of interpretability in many existing deep learning-based diagnostic systems. Therefore, the need for an automated and interpretable brain tumor classification system arises, which is the problem this study aims to address. In this research, a brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images. The model was designed to classify brain tumors into glioma, meningioma, pituitary tumor, and no-tumor categories while providing visual explanations for its predictions using Grad-Class Activation Mapping (Grad-CAM). The performance of the system was evaluated for each tumor category using accuracy, precision, specificity, recall, F1-Score, false positive rate and also the Receiver Operating Characteristic and Area Under Curve (ROC-AUC). Experimental results show that the developed CNN model achieved an overall classification accuracy of 90.6% with an AUC-ROC value of 0.9892, indicating strong discriminative capability across tumor classes. The Grad-CAM visualizations consistently highlighted tumor-affected regions in the MRI images, confirming that the model based its predictions on clinically relevant anatomical structures. The developed model demonstrated effective classification performance and improved interpretability, making it suitable as a reliable decision-support tool for automated brain tumor diagnosis.

Estimation of Municipal Solid Waste Generation Trends From Selected Dumpsite in Ibadan from 2018-2024

K. K. Oyerinde — 2026-07-17

Municipal solid waste (MSW) generation encompasses residential, commercial, institutional, construction, demolition, and sanitation waste streams. This study quantified, characterized, and estimated waste generated at four selected dumpsites in Ibadan, namely Awotan, Lapite, Ajakanga, and Aba Eku, between 2018 and 2024. Annual waste quantities were estimated by projecting Ibadan's population with an exponential growth model and applying a per capita generation rate of 0.55 kg per person per day, after which a 70 percent collection efficiency was used to derive the collected fraction. The collected waste was then distributed across the four sites according to the operative policy allocation ratios for each period, while composition was characterized by applying percentage fractions for each waste category drawn from field and literature sources, and all estimates were validated against Oyo State Waste Management Authority records. On this basis, total waste generated and deposited across all sites was estimated at 10.8 million tonnes, with an average annual input of 1.54 million tonnes. Awotan received the highest annual average (420,000 tonnes), followed by Lapite (380,000 tonnes), Ajakanga (310,000 tonnes), and Aba Eku (210,000 tonnes). Organic matter dominated the waste composition (59%), followed by plastics (10%), paper (9%), textiles (6%), metals (5%), glass (4%), rubber (3%), and others (4%). The 2020 Oyo State waste management policy shift significantly altered the distribution of waste across sites. The Oyo State Government is recommended to institute mandatory waste segregation into organic, recyclable, and inert categories to reduce CO and PM?? emissions from open burning.

Facial Expression-Based Customer Sentiment Analysis for Service Quality Improvement using Deep Learning Techniques

A. O. Esan — 2026-07-17

In today’s customer-centric economy, understanding and responding to customer sentiment is vital for service excellence. Traditional feedback mechanisms, such as surveys and reviews, are often limited by response bias and delayed insights. Hence, this research presents a deep learning approach for improving service delivery through customer sentiment and facial expression analysis. The study leverages specifically MobileNetV2 and InceptionV3, to classify facial expressions into three sentiment classes: Satisfied, not satisfied and Neutral. A local dataset comprising of 900 annotated facial images of locally sourced dataset was curated to address ethnic bias in existing datasets to ensure local relevance while Facial Expression recognition 2013 dataset comprises 48x48 pixel grayscale photos of faces.The methodology involved rigorous data preprocessing, including grayscale normalization, face alignment, and augmentation. MobileNetV2 and InceptionV3 models were trained and evaluated using stratified 80:20 train-test split with categorical cross-entropy as the loss function. Performance was assessed using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Inception reported an accuracy of 94.01% precision of 0.89 recall of 0.98 and an f1 score of 0.94. MobileNet on the other hand reported an accuracy of 90.12%, precision of 0.89, recall of 0.91 and f1-score of 0.91. This shows that inception model outperformed mobileNet in terms of accuracy, precision, recall and f1-score. The results demonstrate the feasibility of using facial expression recognition for sentiment tracking in service environments such as banks, schools, and retail outlets.