OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

Abstract

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework’s performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet’s validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

“…Proposed Method HFs + (ReliefF + PSO) 100% [17] OralNet: Fused Optimal Deep Features 99.50% [110] Neural architecture search and handcrafted descriptors (morphological and non-morphological) 95.20% [111] Handcrafted descriptors (SIFT, SURF, ORB) 92.80% [100] Handcrafted descriptors (morphological and non-morphological) 92.40% [16] Densenet121 91.91% ResNet50 with fine-tuning, multidimensional and multiscale fractal features 99.62% 99.62% [15] ResNet50 (activation_48_relu layer), ReliefF and 5 deep-learned features -99.32% [57] Inception-V3, Fractal Dimension and Lacunarity (DL+HFs) -99.25% [109] CNN for texture 99.10% 98.20% [112] GIST handcrafted descriptor 88.40% 93.70%…”

“…These interpretation problems are mainly caused by human issues, such as subjectivity and fatigue. On the other hand, computer-aided diagnosis (CAD) methods play a fundamental role in this task since they can support specialists with second opinions [8][9][10], especially regarding H&E images [5,8,[11][12][13][14][15][16][17].…”

“…The second group of descriptors consists of deep-learned (DL) features obtained using convolution neural networks (CNNs) [43]. This group has been useful for defining different CAD approaches [15,17,22,[44][45][46][47][48] that consider data representations with multiple levels of abstraction [8,47,49]. The most explored models have provided the best accuracy rates on the ImageNet dataset [50], such as AlexNet [51], GoogleNet [52], VGGNet [53], ResNet [54], Inception [52] and other applications [20,32,55].…”