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Browsing PhD by Author "Ahmad Naeem"

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    A multiclassification deep framework for the diagnosis of skin cancer using dermoscopy images
    (UMT, Lahore, 2024) Ahmad Naeem
    Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. Furthermore, differentiating the specific categories of skin cancers, such as melanoma (Mel), melanocytic nevus (Mn), basal cell carcinoma (bcc), squamous cell carcinoma (Scc), benign keratosis (Bk), Actinic keratosis (Ak), Dermatofibroma (Df) and Vascular lesion (Vl) is also necessary. Thus, two novel methods were designed for the classification of several categories of skin cancer. Firstly, a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images is designed. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. A comparison is performed between the DVFNet, benchmark classifiers and state-of-the-art classifiers. Secondly, a newly developed deep learning model that makes use of two advanced artificial intelligence techniques, Xception ResNet101 (X_R101) for the identification of Mel, Mn, bcc, Scc, Bk, Ak, Df, and VI. To evaluate the performance of the proposed model (X_R101), three publicly available datasets (PH2, DermIS, and HAM10000) are utilized. A comparison is performed between the X_R101 and four benchmark classifiers: MobileNetV2 (BM1), DenseNet201 (BM2), InceptionV3 (BM3), and ResNet50 (BM4) and state-of-the art classifiers. The implementation of borderline SMOTE with X_R101 improves performance substantially. The DVFNet model achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the DVFNet model accuracy. The X_R101 model attains a prediction accuracy of 98.21%. McNemar statistical test is used to validate the X_R101 model accuracy. The accuracy and effectiveness of the proposed models such as DVFNet and X_R101 provide benefits to dermatologists and other healthcare practitioners in terms of timely identification of skin cancer.