Doctoral Dissertations

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http://172.16.30.20:4000/handle/123456789/3603

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Browsing Doctoral Dissertations by Author "BEGHRICHE Tawfiq"

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    Applications of machine learning and deep learning in healthcare: Breast cancer case
    (Université Mohamed Boudiaf - M’sila, 2025-05-25) BEGHRICHE Tawfiq
    Breast cancerrankssecondinfatalityamongwomen.Conventionaldiagnosticmethodsaretime-consuming,exhausting,and expensive,potentiallyleadingtodelayedtreatmentormisdiagnosis.Machinelearning(ML)anddeeplearning(DL)methods haveshownoutstandingpotential.However,theyfacechallengeslikefeatureidentificationandselection,dataimbalance, and parameteroptimization.Unlikemostknownsolutionsthathaveexploredthesestagesforbreastcancerprediction,either separately orinlimitedcombinations,ourapproachsimultaneouslytacklesthesecriticalissuesusingmulti-stageoptimization architecture. Threewell-establishedtechniques,namelycorrelationanalysis-basedfeatureselection(CFS),LASSOregres- sion, andmutualinformation(MI),areusedforFS.Databalancingisperformedusingbothoversamplingandundersampling techniques, includingthesyntheticminorityoversamplingtechnique(SMOTE),k-nearestneighboroversampling(KNNOR), and randomundersampling(RUS).Finally,hyperparameteroptimization(HPO)iscarriedoutbyadoptingvariousmethods including gridsearch,randomsearch,Bayesianoptimization,andsemi-automatictomaximizetheclassificationperfor- mance ofsevenrenownedMLalgorithms(logisticregression,decisiontree,randomforest,supportvectormachine,Naïve Bayes, k-nearestneighbor,andeXtremegradientboosting),andadeepneuralnetwork(DNN).Throughtheexperiments carried outonfourpubliclyavailabledatasets,includingWisconsindiagnosticbreastcancer(WDBC),Wisconsinbreast cancer dataset(WBCD),Wisconsinprognosticbreastcancer(WPBC),andBreastcancercoimbra(BCC),theobtainedresults clearly demonstratethesuperiorityoftheproposedmethodoverthestate-of-the-artmethods.
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    ItemOpen Access
    Applications of machine learning and deep learning in healthcare: Breast cancer case
    (University of M'Sila, 2025) BEGHRICHE Tawfiq
    Breast Cancer (BC) presents a significant global health challenge, highlighting the importance of timely and accurate diagnosis to improve patient outcomes. This thesis explores advanced machine learning (ML) and deep learning (DL) techniques to enhance breast cancer diagnosis, focusing on classification and detection tasks. It addresses data variability, class imbalance, and clinical applicability by developing robust diagnostic models using ultrasound imaging and clinical datasets, such as the Wisconsin Breast Cancer Dataset (WBCD) and the Coimbra dataset. Three primary frameworks for breast cancer detection are developed: (1) An ML-based approach emphasizing feature selection and class imbalance mitigation using techniques such as SMOTE and KNNOR while evaluating seven algorithms, including Support Vector Machine (SVM) and Deep Neural Networks (DNN). (2) An advanced DL-based method utilizing modified transfer learning with pre-trained convolutional neural networks (CNNs) like ResNet50 and MobileNetV2, reused on the Breast Ultrasound Images (BUSI) dataset for improved tumor classification. (3) A hybrid model combining MobileNetV2, DenseNet121, and InceptionV3 to extract features from ultrasound images, refined using LASSO-based feature selection before classification. Models performance is evaluated through metrics like accuracy, sensitivity, specificity, precision, recall, and F1-score, demonstrating the potential of ML and DL to enhance the accuracy and clinical relevance of breast cancer diagnostics, aiding radiologists and clinicians.