SY_104_CustomDataStructures

Team 104- Skin Disease Analysis/core/feature_extraction/extractor.py

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+import os
+import glob
+import cv2
+import numpy as np
+from skimage.feature import local_binary_pattern
+from sklearn.neighbors import KDTree
+
+# Constants
+IMG_SIZE = (128, 128)
+
+# ------------------------------
+# Feature Extraction Function
+# ------------------------------
+def extract_features(image):
+    image = cv2.resize(image, IMG_SIZE)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Color Histogram (RGB)
+    hist = cv2.calcHist([image], [0, 1, 2], None, [8, 8, 8],
+                        [0, 256, 0, 256, 0, 256])
+    hist = cv2.normalize(hist, hist).flatten()
+
+    # LBP Texture
+    lbp = local_binary_pattern(gray, P=8, R=1, method='uniform')
+    (lbp_hist, _) = np.histogram(lbp.ravel(),
+                                 bins=np.arange(0, 11),
+                                 range=(0, 10))
+    lbp_hist = lbp_hist.astype("float")
+    lbp_hist /= (lbp_hist.sum() + 1e-7)
+
+    return np.hstack([hist, lbp_hist])
+
+
+# ------------------------------
+# Dataset Loader + KDTree Builder
+# ------------------------------
+def load_dataset_and_build_kdtree(dataset_path):
+    features_list = []
+    labels_list = []
+    label_map = {}
+    label_names = sorted(os.listdir(dataset_path))
+
+    for idx, disease_folder in enumerate(label_names):
+        full_path = os.path.join(dataset_path, disease_folder)
+        if not os.path.isdir(full_path):
+            continue
+
+        label_clean = disease_folder.split(' ')[0].strip().lower().replace(' ', '_')
+        label_map[idx] = label_clean
+
+        for ext in ('*.jpg', '*.jpeg', '*.png'):
+            for img_path in glob.glob(os.path.join(full_path, ext)):
+                try:
+                    img = cv2.imread(img_path)
+                    if img is None:
+                        continue
+                    feat = extract_features(img)
+                    features_list.append(feat)
+                    labels_list.append(idx)
+                except Exception as e:
+                    print(f"Error with {img_path}: {e}")
+
+    features_array = np.array(features_list)
+    labels_array = np.array(labels_list)
+
+    if len(features_array) > 0:
+        tree = KDTree(features_array)
+        print(f"✅ KD-Tree built with {features_array.shape[0]} feature vectors.")
+        return tree, features_array, labels_array, label_map
+    else:
+        print("⚠️ No features extracted. Please check your image folder paths and formats.")
+        return None, None, None, None

Team 104- Skin Disease Analysis/core/feature_extraction/loading.py

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+# import numpy as np
+# from sklearn.neighbors import KDTree
+# import pickle
+
+# def load_features_and_tree(path='features_tree.pkl'):
+#     with open(path, 'rb') as f:
+#         data = pickle.load(f)
+#     return data['tree'], data['features'], data['labels'], data['label_map']
+
+import pickle
+import numpy as np
+
+def load_kdtree():
+    with open('kdtree.pkl', 'rb') as f:
+        tree = pickle.load(f)
+    return tree
+
+def load_features():
+    return np.load('features.npy')
+
+def load_labels():
+    return np.load('labels.npy')
+
+def load_label_map():
+    with open('label_map.pkl', 'rb') as f:
+        return pickle.load(f)

Team 104- Skin Disease Analysis/core/severity_scoring/severity_minheap.py

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+import csv
+import heapq
+
+# Global dictionary to store per-disease severity weights
+SEVERITY_WEIGHTS = {}
+
+def load_severity_weights(csv_path='data/severity_weights.csv'):
+    global SEVERITY_WEIGHTS
+    with open(csv_path, mode='r', encoding='utf-8') as file:
+        reader = csv.DictReader(file)
+        for row in reader:
+            disease = row["Disease"].strip()
+            SEVERITY_WEIGHTS[disease] = {
+                "pain_level": float(row["pain_level"]),
+                "itching_level": float(row["itching_level"]),
+                "duration_days": float(row["duration_days"]),
+                "area_factor": float(row["area_factor"]),
+                "fever_present": float(row["fever_present"]),
+                "bleeding": float(row["bleeding"]),
+                "spread_rate_factor": float(row["spread_rate_factor"]),
+            }
+
+class SeverityScorer:
+    def __init__(self):
+        self.heap = []  # Heap of (score, disease)
+        self.direct_scores = {}  # Optional raw score addition
+
+    def add_raw_score(self, disease, score):
+        """Add precomputed score (e.g., from image similarity)"""
+        self.direct_scores[disease] = self.direct_scores.get(disease, 0) + score
+
+    def calculate_and_add_score(self, disease, pain_level, itching_level, duration_days, max_duration,
+                                 area_factor, fever_present, bleeding, spread_rate_factor):
+        """
+        Calculates weighted severity using global SEVERITY_WEIGHTS.
+        """
+        if disease not in SEVERITY_WEIGHTS:
+            return
+
+        weights = SEVERITY_WEIGHTS[disease]
+        score = (
+            weights.get("pain_level", 0) * (pain_level / 10) +
+            weights.get("itching_level", 0) * (itching_level / 10) +
+            weights.get("duration_days", 0) * (duration_days / max_duration if max_duration else 0) +
+            weights.get("area_factor", 0) * area_factor +
+            weights.get("fever_present", 0) * int(fever_present) +
+            weights.get("bleeding", 0) * int(bleeding) +
+            weights.get("spread_rate_factor", 0) * spread_rate_factor
+        )
+
+        heapq.heappush(self.heap, (round(score, 4), disease))
+
+    def get_most_severe(self):
+        if self.heap:
+            return heapq.nlargest(1, self.heap)[0]
+        if self.direct_scores:
+            return max(self.direct_scores.items(), key=lambda x: x[1])
+        return None
+
+    def get_all_ranked(self):
+        if self.heap:
+            return heapq.nlargest(len(self.heap), self.heap)
+        return sorted(self.direct_scores.items(), key=lambda x: x[1], reverse=True)

Team 104- Skin Disease Analysis/core/symptom_checker/symptom_trie.py

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+class TrieNode:
+    def __init__(self):
+        self.children = {}
+        self.is_end = False
+        self.diseases = set()
+
+class SymptomTrie:
+    def __init__(self):
+        self.root = TrieNode()
+
+    def insert(self, symptom, disease):
+        node = self.root
+        for char in symptom.lower():
+            if char not in node.children:
+                node.children[char] = TrieNode()
+            node = node.children[char]
+        node.is_end = True
+        node.diseases.add(disease)
+
+    def search(self, prefix):
+        node = self.root
+        for char in prefix.lower():
+            if char in node.children:
+                node = node.children[char]
+            else:
+                return set()
+        return self._collect_diseases(node)
+
+    def _collect_diseases(self, node):
+        results = set(node.diseases)
+        for child in node.children.values():
+            results.update(self._collect_diseases(child))
+        return results

Team 104- Skin Disease Analysis/core/treatment_suggester/treatment_map.py

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+import csv
+
+class TreatmentMap:
+    def __init__(self, csv_path):
+        self.map = {}
+        with open(csv_path, mode='r', encoding='utf-8') as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                disease = row['Disease'].strip()
+                treatments = [t.strip() for t in row['Recommended Treatment'].split(',')]
+                self.map[disease] = treatments  # Disease as key, list of treatments as value
+
+    def get_treatment(self, disease_name):
+        # Get the list of treatments for a given disease name
+        return self.map.get(disease_name, ["Consult a specialist for tailored treatment."])
+
+    def match_symptoms(self, input_symptoms):
+        # Match input symptoms to diseases and return diseases with common symptoms
+        input_set = set(s.strip().lower() for s in input_symptoms.split(','))
+        matches = []
+
+        for disease in self.map.keys():
+            # Placeholder: Add symptom matching logic here
+            matches.append(disease)
+
+        return matches

Team 104- Skin Disease Analysis/data/disease_info.csv

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+Disease,Common Symptoms,Severity Level,Recommended Treatment,ICD-10 Code
+Eczema,"Dry skin, itching, red patches, inflammation, scaling, crusting, redness, cracked skin, swelling",Moderate,"Topical corticosteroids, moisturizers, antihistamines",L20.9
+"Warts, Molluscum, and other Viral Infections","Raised bumps, rough texture, small flesh-colored growths, sometimes itchy or painful, small bumps, brown growths, itching",Mild,"Cryotherapy, salicylic acid, topical antivirals",B07.9
+Melanoma,"New or changing mole, asymmetry, irregular borders, varied colors, bleeding, itching",Severe,"Surgical excision, immunotherapy, targeted therapy",C43.9
+Atopic Dermatitis,"Intense itching, dry scaly skin, red inflamed patches, especially in folds of skin, itching, rashes, swelling, crusting",Moderate,"Topical steroids, emollients, immunomodulators",L20.0
+Basal Cell Carcinoma (BCC),"Pearly or waxy bump, flat flesh-colored lesion, bleeding sore that doesn't heal, scaly area",Severe,"Mohs surgery, radiation therapy, topical chemo",C44.91
+Melanocytic Nevi (NV),"Small and dark brown spots, flat or raised moles, typically uniform in shape and color, ",Mild,"Observation, excision if suspicious or for cosmetic reasons",D22.9
+Benign Keratosis-like Lesions (BKL),"Waxy or wart-like growth, brown/tan/black, scaly surface, can be flat or slightly raised",Mild,"Cryotherapy, curettage, laser removal",L82.1
+"Psoriasis, Lichen Planus, and related diseases","Thick red patches with silvery scales, purple flat-topped bumps, severe itching, cracked skin",Moderate to Severe,"Topical corticosteroids, phototherapy, biologics",L40.9
+Seborrheic Keratoses and other Benign Tumors,"Brown/black growths, waxy/stuck-on appearance, generally painless, ",Mild,"Cryosurgery, curettage, no treatment if asymptomatic",L82.0
+"Tinea, Ringworm, Candidiasis, and other Fungal Infections","Red circular rashes, itching, peeling skin, white patches in mouth or moist areas, scaling, cracking skin",Moderate,"Topical antifungals, oral antifungals in severe cases",B35.9

Team 104- Skin Disease Analysis/data/disease_labels.json

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+{
+    "Disease_0": "Eczema",
+    "Disease_1": "Warts, Molluscum, and other Viral Infections",
+    "Disease_2": "Melanoma",
+    "Disease_3": "Atopic Dermatitis",
+    "Disease_4": "Basal Cell Carcinoma (BCC)",
+    "Disease_5": "Melanocytic Nevi (NV)",
+    "Disease_6": "Benign Keratosis-like Lesions (BKL)",
+    "Disease_7": "Psoriasis, Lichen Planus, and related diseases",
+    "Disease_8": "Seborrheic Keratoses and other Benign Tumors",
+    "Disease_9": "Tinea, Ringworm, Candidiasis, and other Fungal Infections"
+  }
+

Team 104- Skin Disease Analysis/data/severity_weights.csv

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+Disease,pain_level,itching_level,duration_days,area_factor,fever_present,bleeding,spread_rate_factor
+Eczema,0.15,0.25,0.1,0.2,0.05,0.1,0.15
+"Warts, Molluscum, and other Viral Infections",0.2,0.1,0.1,0.25,0.05,0.1,0.2
+Melanoma,0.1,0.05,0.1,0.2,0.2,0.15,0.2
+Atopic Dermatitis,0.1,0.3,0.1,0.15,0.05,0.1,0.2
+Basal Cell Carcinoma (BCC),0.15,0.1,0.15,0.2,0.1,0.15,0.15
+Melanocytic Nevi (NV),0.05,0.05,0.1,0.3,0.05,0.1,0.35
+Benign Keratosis-like Lesions (BKL),0.05,0.1,0.1,0.25,0.05,0.15,0.3
+"Psoriasis, Lichen Planus, and related diseases",0.1,0.25,0.1,0.2,0.05,0.1,0.2
+Seborrheic Keratoses and other Benign Tumors,0.05,0.1,0.1,0.25,0.05,0.1,0.35
+"Tinea, Ringworm, Candidiasis, and other Fungal Infections",0.1,0.25,0.1,0.2,0.05,0.05,0.25