Chollette C. Olisah

Chollette C. Olisah, Sofie V. Cauter

Despite the advancement in computational modeling towards brain tumor segmentation, of which several models have been developed, it is evident from the computational complexity of existing models that performance and efficiency under clinical application scenarios are still limited. Therefore, this paper proposes a tumor segmentation framework. It includes a novel shallow encoder and decoder network named SEDNet for brain tumor segmentation. The highlights of SEDNet include sufficiency in hierarchical convolutional downsampling and selective skip mechanism for cost-efficient and effective brain tumor semantic segmentation, among other features. The preprocessor and optimization function approaches are devised to minimize the uncertainty in feature learning impacted by nontumor slices or empty masks with corresponding brain slices and address class imbalances as well as boundary irregularities of tumors, respectively. Through experiments, SEDNet achieved impressive dice and Hausdorff scores of 0.9308 %, 0.9451 %, and 0.9026 %, and 0.7040 mm, 1.2866 mm, and 0.7762 mm for the non-enhancing tumor core (NTC), peritumoral edema (ED), and enhancing tumor (ET), respectively. This is one of the few works to report segmentation performance on NTC. Furthermore, through transfer learning with initialized SEDNet pre-trained weights, termed SEDNetX, a performance increase is observed. The dice and Hausdorff scores recorded are 0.9336%, 0.9478%, 0.9061%, 0.6983 mm, 1.2691 mm, and 0.7711 mm for NTC, ED, and ET, respectively. With about 1.3 million parameters and impressive performance in comparison to the state-of-the-art, SEDNet(X) is shown to be computationally efficient for real-time clinical diagnosis. The code is available on Github .

Chollette C. Olisah, Ben Trewhella, Bo Li et al.

Fruit ripeness estimation models have for decades depended on spectral index features or colour-based features, such as mean, standard deviation, skewness, colour moments, and/or histograms for learning traits of fruit ripeness. Recently, few studies have explored the use of deep learning techniques to extract features from images of fruits with visible ripeness cues. However, the blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible traits of ripeness when mature and therefore poses great difficulty to fruit pickers. The mature blackberry, to the human eye, is black before, during, and post-ripening. To address this engineering application challenge, this paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier for detecting subtle traits of ripeness in blackberry fruits. The multi-input CNN was created from a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers were optimized for learning traits of ripeness of mature blackberry fruits. The resulting model served as the base for building homogeneous ensemble learners that were ensemble using the stack generalization ensemble (SGE) framework. The input to the network is images acquired with a stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at wavelengths of 700 nm and 770 nm. Through experiments, the proposed model achieved 95.1% accuracy on unseen sets and 90.2% accuracy with in-field conditions. Further experiments reveal that machine sensory is highly and positively correlated to human sensory over blackberry fruit skin texture.

Simisola Odimayo, Chollette C. Olisah, Khadija Mohammed

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Chollette C. Olisah, Lyndon Smith, Melvyn Smith et al.

Crop yield prediction has been modeled on the assumption that there is no interaction between weather and soil variables. However, this paper argues that an interaction exists, and it can be finely modelled using the Kendall Correlation coefficient. Given the nonlinearity of the interaction between weather and soil variables, a deep neural network regressor (DNNR) is carefully designed with consideration to the depth, number of neurons of the hidden layers, and the hyperparameters with their optimizations. Additionally, a new metric, the average of absolute root squared error (ARSE) is proposed to combine the strengths of root mean square error (RMSE) and mean absolute error (MAE). With the ARSE metric, the proposed DNNR(s), optimised random forest regressor (RFR) and the extreme gradient boosting regressor (XGBR) achieved impressively small yield errors, 0.0172 t/ha, and 0.0243 t/ha, 0.0001 t/ha, and 0.001 t/ha, respectively. However, the DNNR(s), with changes to the explanatory variables to ensure generalizability to unforeseen data, DNNR(s) performed best. Further analysis reveals that a strong interaction does exist between weather and soil variables. Precisely, yield is observed to increase when precipitation is reduced and silt increased, and vice-versa. However, the degree of decrease or increase is not quantified in this paper. Contrary to existing yield models targeted towards agricultural policies and global food security, the goal of the proposed corn yield model is to empower the smallholder farmer to farm smartly and intelligently, thus the prediction model is integrated into a mobile application that includes education, and a farmer-to-market access module.

Paul Brimoh, Chollette C. Olisah

A genuine signer's signature is naturally unstable even at short time-intervals whereas, expert forgers always try to perfectly mimic a genuine signer's signature. This presents a challenge which puts a genuine signer at risk of being denied access, while a forge signer is granted access. The implication is a high false acceptance rate (FAR) which is the percentage of forge signature classified as belonging to a genuine class. Existing work have only scratched the surface of signature verification because the misclassification error remains high. In this paper, a consensus-threshold distance-based classifier criterion is proposed for offline writer-dependent signature verification. Using features extracted from SigNet and SigNet-F deep convolutional neural network models, the proposed classifier minimizes FAR. This is demonstrated via experiments on four datasets: GPDS-300, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-300, the consensus threshold classifier improves the state-of-the-art performance by achieving a 1.27% FAR compared to 8.73% and 17.31% recorded in literature. This performance is consistent across other datasets and guarantees that the risk of imposters gaining access to sensitive documents or transactions is minimal.

Chollette C. Olisah, Lyndon Smith

Deep networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessors. Therefore, in this paper, the network's preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; HSV, CIE L*a*b* and YCBCR, grey-level resolution preprocessors; full-based and plane-based image quantization, illumination normalization and insensitive feature preprocessing using: histogram equalization (HE), local contrast normalization (LN) and complete face structural pattern (CFSP). To achieve fixed network parameters, CNNs with transfer learning is employed. Knowledge from the high-level feature vectors of the Inception-V3 network is transferred to offline preprocessed LFW target data; and features trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with HE, full-based and plane-based quantization, rgbGELog, and YCBCR, preprocessors before feeding it to CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. The plane-based image quantization is found to increase the homogeneity of neighborhood pixels and utilizes reduced bit depth for better storage efficiency.

Chollette C. Olisah, Solomon Nunoo, Peter Ofedebe et al.

Beneath the uncertain primitive visual features of face images are the primitive intrinsic structural patterns (PISP) essential for characterizing a sample face discriminative attributes. It is on this basis that this paper presents a simple yet effective facial descriptor formed from derivatives of Gaussian and Gabor Wavelets. The new descriptor is coined local edge gradient Gabor magnitude (LEGGM) pattern. LEGGM first uncovers the PISP locked in every pixel through determining the pixel gradient in relation to its neighbors using the Derivatives of Gaussians. Then, the resulting output is embedded into the global appearance of the face which are further processed using Gabor wavelets in order to express its frequency characteristics. Additionally, we adopted various subspace models for dimensionality reduction in order to ascertain the best fit model for reporting a more effective representation of the LEGGM patterns. The proposed descriptor-based face recognition method is evaluated on three databases: Plastic surgery, LFW, and GT face databases. Through experiments, using a base classifier, the efficacy of the proposed method is demonstrated, especially in the case of plastic surgery database. The heterogeneous database, which we created to typify real-world scenario, show that the proposed method is to an extent insensitive to image formation factors with impressive recognition performances.